{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Data Cleaning_Capstone Project.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Raizel820/Raizel820-Capstone_Project_Machine_Learning/blob/master/Data_Cleaning_Capstone_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9BOktGg7ghg",
        "colab_type": "code",
        "outputId": "8a3b12a3-5b78-4004-c253-2c29d3b998cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        }
      },
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import subprocess as sp\n",
        "import sys,os,shutil,pickle,time\n",
        "from os.path import join\n",
        "plt.style.use('default')\n",
        "# mount Google Drive\n",
        "from os.path import expanduser\n",
        "gd_path=join(expanduser(\"~\"),'gd')\n",
        "if not os.path.isdir(gd_path):\n",
        "    try:\n",
        "        # load Google Drive\n",
        "        from google.colab import drive,files\n",
        "        drive.mount('/drive')\n",
        "        sp.call('ln -s /drive/My\\ Drive '+gd_path, shell=True)\n",
        "    except:\n",
        "        print('unable to find Google Drive Folder')\n",
        "os.chdir(gd_path+'/MachineLearning/car_data')\n",
        "os.listdir('.')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /drive\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['craigslistVehicles_full.csv', 'cars_cleaned.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UkeaKWIE06i6",
        "colab_type": "text"
      },
      "source": [
        "The dataset is taken from https://www.kaggle.com/austinreese/craigslist-carstrucks-data#craigslistVehicles.csv"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjNZDhyK8DYW",
        "colab_type": "code",
        "outputId": "be48d64f-0b7d-4cd6-8f91-b94382f76f61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "df= pd.read_csv('craigslistVehicles_full.csv')\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>url</th>\n",
              "      <th>city</th>\n",
              "      <th>city_url</th>\n",
              "      <th>price</th>\n",
              "      <th>year</th>\n",
              "      <th>manufacturer</th>\n",
              "      <th>make</th>\n",
              "      <th>condition</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>fuel</th>\n",
              "      <th>odometer</th>\n",
              "      <th>title_status</th>\n",
              "      <th>transmission</th>\n",
              "      <th>VIN</th>\n",
              "      <th>drive</th>\n",
              "      <th>size</th>\n",
              "      <th>type</th>\n",
              "      <th>paint_color</th>\n",
              "      <th>image_url</th>\n",
              "      <th>desc</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>https://abilene.craigslist.org/cto/d/aspermont...</td>\n",
              "      <td>abilene, TX</td>\n",
              "      <td>https://abilene.craigslist.org</td>\n",
              "      <td>9000</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>suburban lt2</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>217743.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>1GFNC26099R211276</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>SUV</td>\n",
              "      <td>white</td>\n",
              "      <td>https://images.craigslist.org/00909_5P6f1E6zHl...</td>\n",
              "      <td>2WD 1/2 ton\\nLeather Captains Chairs\\nIn good ...</td>\n",
              "      <td>33.130100</td>\n",
              "      <td>-100.234400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>https://abilene.craigslist.org/ctd/d/liberty-h...</td>\n",
              "      <td>abilene, TX</td>\n",
              "      <td>https://abilene.craigslist.org</td>\n",
              "      <td>31999</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>ram</td>\n",
              "      <td>2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>diesel</td>\n",
              "      <td>NaN</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/00u0u_24XW8uhLYP...</td>\n",
              "      <td>www.GETADIESEL.com\\n2012 RAM 2500 SLT 4X4 MEGA...</td>\n",
              "      <td>30.648368</td>\n",
              "      <td>-97.862949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>https://abilene.craigslist.org/ctd/d/liberty-h...</td>\n",
              "      <td>abilene, TX</td>\n",
              "      <td>https://abilene.craigslist.org</td>\n",
              "      <td>16990</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>ram</td>\n",
              "      <td>3500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>diesel</td>\n",
              "      <td>NaN</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://images.craigslist.org/00l0l_H1UKTJ4sTI...</td>\n",
              "      <td>www.GETADIESEL.com\\n2003 DODGE RAM 3500 LARAMI...</td>\n",
              "      <td>30.648516</td>\n",
              "      <td>-97.862434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>https://abilene.craigslist.org/cto/d/merkel-20...</td>\n",
              "      <td>abilene, TX</td>\n",
              "      <td>https://abilene.craigslist.org</td>\n",
              "      <td>6000</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>gmc</td>\n",
              "      <td>sierra 1500</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>195000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>pickup</td>\n",
              "      <td>white</td>\n",
              "      <td>https://images.craigslist.org/00M0M_2wKzc7dKLU...</td>\n",
              "      <td>2002 GMC Sierra Extended Cab Truck For Sale! R...</td>\n",
              "      <td>32.444400</td>\n",
              "      <td>-99.992400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>https://abilene.craigslist.org/cto/d/breckenri...</td>\n",
              "      <td>abilene, TX</td>\n",
              "      <td>https://abilene.craigslist.org</td>\n",
              "      <td>37000</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>3500</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>178000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>silver</td>\n",
              "      <td>https://images.craigslist.org/00r0r_eW1YWlKfCG...</td>\n",
              "      <td>2012 Chevrolet 3500 178k miles, runs and drive...</td>\n",
              "      <td>32.781689</td>\n",
              "      <td>-98.942248</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 url  ...        long\n",
              "0  https://abilene.craigslist.org/cto/d/aspermont...  ... -100.234400\n",
              "1  https://abilene.craigslist.org/ctd/d/liberty-h...  ...  -97.862949\n",
              "2  https://abilene.craigslist.org/ctd/d/liberty-h...  ...  -97.862434\n",
              "3  https://abilene.craigslist.org/cto/d/merkel-20...  ...  -99.992400\n",
              "4  https://abilene.craigslist.org/cto/d/breckenri...  ...  -98.942248\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFfkveoiFzT1",
        "colab_type": "code",
        "outputId": "ed558852-5802-426e-fcf8-415f9065365e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(550313, 22)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JD6oKshdgBeA",
        "colab_type": "code",
        "outputId": "b5b693ef-0c94-4d01-8df9-018bc09172bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 550313 entries, 0 to 550312\n",
            "Data columns (total 22 columns):\n",
            "url             550313 non-null object\n",
            "city            550313 non-null object\n",
            "city_url        550313 non-null object\n",
            "price           550313 non-null int64\n",
            "year            548826 non-null float64\n",
            "manufacturer    523398 non-null object\n",
            "make            540636 non-null object\n",
            "condition       300239 non-null object\n",
            "cylinders       331316 non-null object\n",
            "fuel            545572 non-null object\n",
            "odometer        439513 non-null float64\n",
            "title_status    546289 non-null object\n",
            "transmission    546258 non-null object\n",
            "VIN             311075 non-null object\n",
            "drive           384475 non-null object\n",
            "size            184057 non-null object\n",
            "type            391134 non-null object\n",
            "paint_color     370292 non-null object\n",
            "image_url       550287 non-null object\n",
            "desc            550283 non-null object\n",
            "lat             538523 non-null float64\n",
            "long            538523 non-null float64\n",
            "dtypes: float64(4), int64(1), object(17)\n",
            "memory usage: 92.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZ2Gq34S-09P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df= df.drop(columns=['url','image_url', 'lat', 'long', 'city_url', 'desc', 'city', 'VIN'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezO8sck5_VDp",
        "colab_type": "code",
        "outputId": "2021d250-c5d9-40b1-9208-9605f6942d88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "list(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['price',\n",
              " 'year',\n",
              " 'manufacturer',\n",
              " 'make',\n",
              " 'condition',\n",
              " 'cylinders',\n",
              " 'fuel',\n",
              " 'odometer',\n",
              " 'title_status',\n",
              " 'transmission',\n",
              " 'drive',\n",
              " 'size',\n",
              " 'type',\n",
              " 'paint_color']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsB7Br7aNlbz",
        "colab_type": "code",
        "outputId": "1f60b9fa-93b5-4899-b2d5-00d241a21742",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "source": [
        "null_values=df.isnull().sum()\n",
        "null_values=pd.DataFrame(null_values,columns=['null'])\n",
        "j=1\n",
        "sum_tot=len(df)\n",
        "null_values['percent']=null_values['null']/sum_tot\n",
        "round(null_values*100,3).sort_values('percent',ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>null</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>size</th>\n",
              "      <td>36625600</td>\n",
              "      <td>66.554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>25007400</td>\n",
              "      <td>45.442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cylinders</th>\n",
              "      <td>21899700</td>\n",
              "      <td>39.795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paint_color</th>\n",
              "      <td>18002100</td>\n",
              "      <td>32.712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drive</th>\n",
              "      <td>16583800</td>\n",
              "      <td>30.135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>15917900</td>\n",
              "      <td>28.925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odometer</th>\n",
              "      <td>11080000</td>\n",
              "      <td>20.134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturer</th>\n",
              "      <td>2691500</td>\n",
              "      <td>4.891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make</th>\n",
              "      <td>967700</td>\n",
              "      <td>1.758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel</th>\n",
              "      <td>474100</td>\n",
              "      <td>0.862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission</th>\n",
              "      <td>405500</td>\n",
              "      <td>0.737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>402400</td>\n",
              "      <td>0.731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>148700</td>\n",
              "      <td>0.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  null  percent\n",
              "size          36625600   66.554\n",
              "condition     25007400   45.442\n",
              "cylinders     21899700   39.795\n",
              "paint_color   18002100   32.712\n",
              "drive         16583800   30.135\n",
              "type          15917900   28.925\n",
              "odometer      11080000   20.134\n",
              "manufacturer   2691500    4.891\n",
              "make            967700    1.758\n",
              "fuel            474100    0.862\n",
              "transmission    405500    0.737\n",
              "title_status    402400    0.731\n",
              "year            148700    0.270\n",
              "price                0    0.000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef90a9y1FU4G",
        "colab_type": "text"
      },
      "source": [
        "Let's eliminate extreme values from price column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFa-r7IfsVb4",
        "colab_type": "code",
        "outputId": "19a235f4-6618-4273-c02b-c6b00781d088",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df[df.price == 0].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48708, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_9C6QuwFLod",
        "colab_type": "code",
        "outputId": "431b1d4c-97e6-435f-fb25-a1c10b603360",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df[df.price > 100000].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(580, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8pmETYHoDy_",
        "colab_type": "code",
        "outputId": "24e06ce7-5127-456c-b90b-600e64740471",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df[df.price < 750].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61726, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ll5qy9kJrmSw",
        "colab_type": "code",
        "outputId": "638f2ddd-c56f-4b66-8db2-ae898eeb10ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df=df[(df.price < 100000) & (df.price >= 750 )]\n",
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(488003, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehlPv8hkFt9Y",
        "colab_type": "text"
      },
      "source": [
        "Odometers higher than 300k and lower than 10, and models earlier than 1985 may not be good observations for a robust prediction:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAE__X0Jo_gv",
        "colab_type": "code",
        "outputId": "60fb94d1-d1a4-4ca7-c19d-1dde2e6a0ee2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df[df.odometer < 1000].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8113, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbS7Ge97oazZ",
        "colab_type": "code",
        "outputId": "b3f61f1a-dbb7-4124-eeed-31fad32b5e2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        }
      },
      "source": [
        "df[df.odometer < 1000].sample(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>year</th>\n",
              "      <th>manufacturer</th>\n",
              "      <th>make</th>\n",
              "      <th>condition</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>fuel</th>\n",
              "      <th>odometer</th>\n",
              "      <th>title_status</th>\n",
              "      <th>transmission</th>\n",
              "      <th>drive</th>\n",
              "      <th>size</th>\n",
              "      <th>type</th>\n",
              "      <th>paint_color</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>66484</th>\n",
              "      <td>27000</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>f-150 lariat automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>pickup</td>\n",
              "      <td>brown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>388446</th>\n",
              "      <td>98850</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>INTERNATIONAL MV MOVING VAN</td>\n",
              "      <td>new</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>282.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>truck</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46659</th>\n",
              "      <td>2500</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>focus</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>106.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>fwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coupe</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225724</th>\n",
              "      <td>7000</td>\n",
              "      <td>1957.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>apache</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>1.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>4wd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6515</th>\n",
              "      <td>28900</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>mustang gt coupe 2d</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>779.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coupe</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44373</th>\n",
              "      <td>7500</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>bmw</td>\n",
              "      <td>x5</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>145.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437848</th>\n",
              "      <td>6600</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>mr2 turbo</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>242.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>rwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>462798</th>\n",
              "      <td>11495</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>charger</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>1.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sedan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26887</th>\n",
              "      <td>32314</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>mazda</td>\n",
              "      <td>miata rf club</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>3.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coupe</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383334</th>\n",
              "      <td>16500</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>hr-v</td>\n",
              "      <td>like new</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>350.0</td>\n",
              "      <td>rebuilt</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SUV</td>\n",
              "      <td>grey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177767</th>\n",
              "      <td>3995</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>buick</td>\n",
              "      <td>century</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>0.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217838</th>\n",
              "      <td>2900</td>\n",
              "      <td>2005.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>escape</td>\n",
              "      <td>good</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>148.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SUV</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>345219</th>\n",
              "      <td>33900</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>caravan</td>\n",
              "      <td>excellent</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>10.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141794</th>\n",
              "      <td>2995</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>nissan</td>\n",
              "      <td>altima</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>95.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>471044</th>\n",
              "      <td>1100</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>avenger</td>\n",
              "      <td>good</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>120.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coupe</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>524806</th>\n",
              "      <td>6200</td>\n",
              "      <td>1922.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>caberet model t</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>1.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>convertible</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214031</th>\n",
              "      <td>6900</td>\n",
              "      <td>2004.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>tacoma</td>\n",
              "      <td>good</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>300.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>truck</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51464</th>\n",
              "      <td>9500</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>rav4</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>84.0</td>\n",
              "      <td>lien</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SUV</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460733</th>\n",
              "      <td>14500</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>pickup</td>\n",
              "      <td>excellent</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>0.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>385677</th>\n",
              "      <td>1399</td>\n",
              "      <td>2005.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>1500</td>\n",
              "      <td>fair</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>129.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>truck</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        price    year manufacturer  ...       size         type paint_color\n",
              "66484   27000  2016.0         ford  ...        NaN       pickup       brown\n",
              "388446  98850  2020.0          NaN  ...        NaN        truck       white\n",
              "46659    2500  2001.0         ford  ...        NaN        coupe        blue\n",
              "225724   7000  1957.0    chevrolet  ...   mid-size       pickup         red\n",
              "6515    28900  2016.0         ford  ...        NaN        coupe         red\n",
              "44373    7500  2009.0          bmw  ...        NaN          NaN         NaN\n",
              "437848   6600  1991.0       toyota  ...        NaN          NaN       black\n",
              "462798  11495  2013.0        dodge  ...        NaN        sedan         NaN\n",
              "26887   32314  2019.0        mazda  ...        NaN        coupe         NaN\n",
              "383334  16500  2019.0        honda  ...        NaN          SUV        grey\n",
              "177767   3995  2003.0        buick  ...        NaN          NaN         NaN\n",
              "217838   2900  2005.0         ford  ...        NaN          SUV        blue\n",
              "345219  33900  2019.0        dodge  ...        NaN          NaN      silver\n",
              "141794   2995  2006.0       nissan  ...        NaN          NaN         NaN\n",
              "471044   1100  1998.0        dodge  ...        NaN        coupe         NaN\n",
              "524806   6200  1922.0         ford  ...  full-size  convertible       black\n",
              "214031   6900  2004.0       toyota  ...  full-size        truck       white\n",
              "51464    9500  2010.0       toyota  ...        NaN          SUV         red\n",
              "460733  14500  1986.0    chevrolet  ...        NaN          NaN         NaN\n",
              "385677   1399  2005.0    chevrolet  ...  full-size        truck       white\n",
              "\n",
              "[20 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGicoglnr5Km",
        "colab_type": "code",
        "outputId": "d55dbd68-b1a4-478d-c112-c2d361b80023",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df[df.odometer >300000].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3066, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIu4EX29F92a",
        "colab_type": "code",
        "outputId": "80dde6e9-f375-4ddf-e117-8d10867b7628",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df=df[(df.odometer < 300000) & (df.odometer > 1000)]\n",
        "df=df[df.year>1985]\n",
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(375332, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KaAuWZMbt7Ch",
        "colab_type": "code",
        "outputId": "15169c70-522f-416e-8efa-3ae32dff749f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "source": [
        "null_values=df.isnull().sum()\n",
        "null_values=pd.DataFrame(null_values,columns=['null'])\n",
        "j=1\n",
        "sum_tot=len(df)\n",
        "null_values['percent']=null_values['null']/sum_tot\n",
        "round(null_values*100,3).sort_values('percent',ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>null</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>size</th>\n",
              "      <td>23864700</td>\n",
              "      <td>63.583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>15331600</td>\n",
              "      <td>40.848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cylinders</th>\n",
              "      <td>12797900</td>\n",
              "      <td>34.098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paint_color</th>\n",
              "      <td>9108700</td>\n",
              "      <td>24.268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drive</th>\n",
              "      <td>8094900</td>\n",
              "      <td>21.567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>7308300</td>\n",
              "      <td>19.472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturer</th>\n",
              "      <td>1138100</td>\n",
              "      <td>3.032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make</th>\n",
              "      <td>375000</td>\n",
              "      <td>0.999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel</th>\n",
              "      <td>347800</td>\n",
              "      <td>0.927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>328100</td>\n",
              "      <td>0.874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission</th>\n",
              "      <td>321800</td>\n",
              "      <td>0.857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odometer</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  null  percent\n",
              "size          23864700   63.583\n",
              "condition     15331600   40.848\n",
              "cylinders     12797900   34.098\n",
              "paint_color    9108700   24.268\n",
              "drive          8094900   21.567\n",
              "type           7308300   19.472\n",
              "manufacturer   1138100    3.032\n",
              "make            375000    0.999\n",
              "fuel            347800    0.927\n",
              "title_status    328100    0.874\n",
              "transmission    321800    0.857\n",
              "price                0    0.000\n",
              "year                 0    0.000\n",
              "odometer             0    0.000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFJu_wSrhjvi",
        "colab_type": "text"
      },
      "source": [
        "Now, it's time to fill some missing data in a meaningful way."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WiIB4mUcivFt",
        "colab_type": "text"
      },
      "source": [
        "##### Dealing with missing 'condition' values\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxWkf36JBbHO",
        "colab_type": "code",
        "outputId": "3788135a-a0d7-49af-a660-d553fbae5f60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df['condition'].isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "153316"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SRPQnOvRjBuB",
        "colab_type": "code",
        "outputId": "d6752c12-70d4-466b-d895-2c97164d67f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df.loc[(df['year'] >= 2017)]['condition'].isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7p7sriQjcyZ",
        "colab_type": "code",
        "outputId": "5d7f27ba-65b8-4d47-fd0b-699fe6c2d2c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "df.condition.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "excellent    107483\n",
              "good          84178\n",
              "like new      21873\n",
              "fair           7387\n",
              "new             625\n",
              "salvage         470\n",
              "Name: condition, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rL5naMV3jM2b",
        "colab_type": "text"
      },
      "source": [
        "###### I will label all cars:\n",
        "##### after 2019 as \"new\"\n",
        "##### 2017-2019 as \"like new\"\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATJYoEdsjWfZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.loc[df.year>=2019, 'condition'] = df.loc[df.year>=2019, 'condition'].fillna('new')\n",
        "df.loc[df.year>=2017, 'condition'] = df.loc[df.year>=2017, 'condition'].fillna('like new')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yw5ZiMq0kSv6",
        "colab_type": "code",
        "outputId": "c1c9ba45-0250-4fb6-a423-2917d86b8990",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "df.condition.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "excellent    107483\n",
              "good          84178\n",
              "like new      43050\n",
              "fair           7387\n",
              "new            3448\n",
              "salvage         470\n",
              "Name: condition, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofyX28oGOAzZ",
        "colab_type": "text"
      },
      "source": [
        "##### I will assume that milage of the car affects it's condition. Therefore, I will fill missing values depending on odometer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qP11RdMfOPH1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "excellent_odo_mean = df[df['condition'] == 'excellent']['odometer'].mean()\n",
        "good_odo_mean = df[df['condition'] == 'good']['odometer'].mean()\n",
        "like_new_odo_mean = df[df['condition'] == 'like new']['odometer'].mean()\n",
        "salvage_odo_mean = df[df['condition'] == 'salvage']['odometer'].mean()\n",
        "fair_odo_mean = df[df['condition'] == 'fair']['odometer'].mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_Kou9RFQ_mR",
        "colab_type": "code",
        "outputId": "25ff603e-bdba-4c0b-ca80-db1c43b7fc55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "print('Like new average odometer:', round( like_new_odo_mean,2))\n",
        "print('Excellent average odometer:', round( excellent_odo_mean,2))\n",
        "print('Good average odometer:', round( good_odo_mean,2))\n",
        "print('Fair average odometer:', round( fair_odo_mean,2))\n",
        "print('Salvage average odometer:', round( salvage_odo_mean,2))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Like new average odometer: 56933.98\n",
            "Excellent average odometer: 107756.25\n",
            "Good average odometer: 119964.83\n",
            "Fair average odometer: 172456.97\n",
            "Salvage average odometer: 119788.47\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TgtGBcBJdEv",
        "colab_type": "code",
        "outputId": "0330424e-2fb9-4f92-d2dd-fda6a692c345",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "df.loc[df['odometer'] <= like_new_odo_mean, 'condition'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "like new     27612\n",
              "excellent    18862\n",
              "good         16609\n",
              "new           3130\n",
              "fair           152\n",
              "salvage        111\n",
              "Name: condition, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILjZe0HnGkIk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.loc[df['odometer'] <= like_new_odo_mean, 'condition'] = df.loc[df['odometer'] <= like_new_odo_mean, 'condition'].fillna('like new')\n",
        "\n",
        "df.loc[df['odometer'] >= fair_odo_mean, 'condition'] = df.loc[df['odometer'] >= fair_odo_mean, 'condition'].fillna('fair')\n",
        "\n",
        "df.loc[((df['odometer'] > like_new_odo_mean) & \n",
        "       (df['odometer'] <= excellent_odo_mean)), 'condition'] = df.loc[((df['odometer'] > like_new_odo_mean) & \n",
        "       (df['odometer'] <= excellent_odo_mean)), 'condition'].fillna('excellent')\n",
        "\n",
        "df.loc[((df['odometer'] > excellent_odo_mean) & \n",
        "       (df['odometer'] <= good_odo_mean)), 'condition'] = df.loc[((df['odometer'] > excellent_odo_mean) & \n",
        "       (df['odometer'] <= good_odo_mean)), 'condition'].fillna('good')\n",
        "\n",
        "df.loc[((df['odometer'] > good_odo_mean) & \n",
        "       (df['odometer'] <= fair_odo_mean)), 'condition'] = df.loc[((df['odometer'] > good_odo_mean) & \n",
        "       (df['odometer'] <= fair_odo_mean)), 'condition'].fillna('salvage')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hboS_uMwCSfJ",
        "colab_type": "code",
        "outputId": "3cf12c91-e3de-44d5-8b15-d6baa857e2f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "source": [
        "null_values=df.isnull().sum()\n",
        "null_values=pd.DataFrame(null_values,columns=['null'])\n",
        "j=1\n",
        "sum_tot=len(df)\n",
        "null_values['percent']=null_values['null']/sum_tot\n",
        "round(null_values*100,3).sort_values('percent',ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>null</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>size</th>\n",
              "      <td>23864700</td>\n",
              "      <td>63.583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cylinders</th>\n",
              "      <td>12797900</td>\n",
              "      <td>34.098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paint_color</th>\n",
              "      <td>9108700</td>\n",
              "      <td>24.268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drive</th>\n",
              "      <td>8094900</td>\n",
              "      <td>21.567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>7308300</td>\n",
              "      <td>19.472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturer</th>\n",
              "      <td>1138100</td>\n",
              "      <td>3.032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make</th>\n",
              "      <td>375000</td>\n",
              "      <td>0.999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel</th>\n",
              "      <td>347800</td>\n",
              "      <td>0.927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>328100</td>\n",
              "      <td>0.874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission</th>\n",
              "      <td>321800</td>\n",
              "      <td>0.857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odometer</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  null  percent\n",
              "size          23864700   63.583\n",
              "cylinders     12797900   34.098\n",
              "paint_color    9108700   24.268\n",
              "drive          8094900   21.567\n",
              "type           7308300   19.472\n",
              "manufacturer   1138100    3.032\n",
              "make            375000    0.999\n",
              "fuel            347800    0.927\n",
              "title_status    328100    0.874\n",
              "transmission    321800    0.857\n",
              "price                0    0.000\n",
              "year                 0    0.000\n",
              "condition            0    0.000\n",
              "odometer             0    0.000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mh-8nEOPC9UM",
        "colab_type": "text"
      },
      "source": [
        "#### Dealing with missing 'cylinders' values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VmYjgVM6C6Rd",
        "colab_type": "code",
        "outputId": "df1ee6a5-58fd-4a85-c222-a6baa82acc17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        }
      },
      "source": [
        "df.cylinders.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6 cylinders     90977\n",
              "4 cylinders     76361\n",
              "8 cylinders     74885\n",
              "5 cylinders      2423\n",
              "10 cylinders     1291\n",
              "other             917\n",
              "3 cylinders       355\n",
              "12 cylinders      144\n",
              "Name: cylinders, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4kwLPgDwEGf",
        "colab_type": "text"
      },
      "source": [
        "With time series data, using pad/ffill is extremely common so that the “last known value” is available at every time point. In other words, ffill propagates last valid observation forward to next valid. Therefore, **rest of the missing values will be filled with 'ffill' method.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvzVOWybwDm-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['transmission'] = df['transmission'].fillna(method='ffill')\n",
        "df['cylinders'] = df['cylinders'].fillna(method='ffill')\n",
        "df['title_status'] = df['title_status'].fillna(method='ffill')\n",
        "df['fuel'] = df['fuel'].fillna(method='ffill')\n",
        "df['size'] = df['size'].fillna(method='ffill')\n",
        "df['paint_color'] = df['paint_color'].fillna(method='ffill')\n",
        "df['drive'] = df['drive'].fillna(method='ffill')\n",
        "df['manufacturer'] = df['manufacturer'].fillna(method='ffill')\n",
        "df['make'] = df['make'].fillna(method='ffill')\n",
        "df['type'] = df['type'].fillna(method='ffill')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcJO53u_2RFp",
        "colab_type": "code",
        "outputId": "77817278-c23a-48dd-9d8b-54716699c3b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price           0\n",
              "year            0\n",
              "manufacturer    0\n",
              "make            0\n",
              "condition       0\n",
              "cylinders       0\n",
              "fuel            0\n",
              "odometer        0\n",
              "title_status    0\n",
              "transmission    0\n",
              "drive           0\n",
              "size            0\n",
              "type            0\n",
              "paint_color     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zte7KqW1DVIc",
        "colab_type": "code",
        "outputId": "98a60650-d736-40d8-80b4-e1ba70e16e75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "source": [
        "null_values=df.isnull().sum()\n",
        "null_values=pd.DataFrame(null_values,columns=['null'])\n",
        "j=1\n",
        "sum_tot=len(df)\n",
        "null_values['percent']=null_values['null']/sum_tot\n",
        "round(null_values*100,3).sort_values('percent',ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>null</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturer</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>make</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cylinders</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odometer</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drive</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>size</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paint_color</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              null  percent\n",
              "price            0      0.0\n",
              "year             0      0.0\n",
              "manufacturer     0      0.0\n",
              "make             0      0.0\n",
              "condition        0      0.0\n",
              "cylinders        0      0.0\n",
              "fuel             0      0.0\n",
              "odometer         0      0.0\n",
              "title_status     0      0.0\n",
              "transmission     0      0.0\n",
              "drive            0      0.0\n",
              "size             0      0.0\n",
              "type             0      0.0\n",
              "paint_color      0      0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mv1SdENIDhyX",
        "colab_type": "code",
        "outputId": "8fba9a6c-b22f-4553-a165-6e4d9488ab4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 375332 entries, 0 to 550312\n",
            "Data columns (total 14 columns):\n",
            "price           375332 non-null int64\n",
            "year            375332 non-null float64\n",
            "manufacturer    375332 non-null object\n",
            "make            375332 non-null object\n",
            "condition       375332 non-null object\n",
            "cylinders       375332 non-null object\n",
            "fuel            375332 non-null object\n",
            "odometer        375332 non-null float64\n",
            "title_status    375332 non-null object\n",
            "transmission    375332 non-null object\n",
            "drive           375332 non-null object\n",
            "size            375332 non-null object\n",
            "type            375332 non-null object\n",
            "paint_color     375332 non-null object\n",
            "dtypes: float64(2), int64(1), object(11)\n",
            "memory usage: 43.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBeLFJhj3K2Q",
        "colab_type": "code",
        "outputId": "6cd30148-fc05-4c39-eca4-683f7833c4da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(375332, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izViYbCV4Vza",
        "colab_type": "text"
      },
      "source": [
        "By looking at above 'type' and 'drive' values, we can see that there is no heavily dominant sub category. Therefore, after this point, all rows that has a missing value will be deleted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_V39Y7Pr5Aws",
        "colab_type": "text"
      },
      "source": [
        "# Exploring the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_Fx6qei5F50",
        "colab_type": "code",
        "outputId": "0a956da8-8ba5-48fd-ea0b-1f20d58bb9c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df.sample(40)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>year</th>\n",
              "      <th>manufacturer</th>\n",
              "      <th>make</th>\n",
              "      <th>condition</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>fuel</th>\n",
              "      <th>odometer</th>\n",
              "      <th>title_status</th>\n",
              "      <th>transmission</th>\n",
              "      <th>drive</th>\n",
              "      <th>size</th>\n",
              "      <th>type</th>\n",
              "      <th>paint_color</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>243303</th>\n",
              "      <td>7897</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>honda</td>\n",
              "      <td>accord</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>167000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>464061</th>\n",
              "      <td>6699</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>bmw</td>\n",
              "      <td>528i</td>\n",
              "      <td>excellent</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>116000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203706</th>\n",
              "      <td>2950</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>chrysler</td>\n",
              "      <td>sebring touring</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>202000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11384</th>\n",
              "      <td>5595</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>chrysler</td>\n",
              "      <td>town &amp; country</td>\n",
              "      <td>good</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>136498.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>van</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246093</th>\n",
              "      <td>1800</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>taurus</td>\n",
              "      <td>excellent</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>76700.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>SUV</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500457</th>\n",
              "      <td>4500</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>mazda</td>\n",
              "      <td>miata</td>\n",
              "      <td>good</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>102500.0</td>\n",
              "      <td>salvage</td>\n",
              "      <td>manual</td>\n",
              "      <td>rwd</td>\n",
              "      <td>compact</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36085</th>\n",
              "      <td>29995</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>silverado lt crew cab</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>81406.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>345164</th>\n",
              "      <td>27991</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>mustang</td>\n",
              "      <td>like new</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>32288.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>rwd</td>\n",
              "      <td>compact</td>\n",
              "      <td>convertible</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276670</th>\n",
              "      <td>5275</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>kia</td>\n",
              "      <td>forte koup</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>114587.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>fwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>coupe</td>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>328190</th>\n",
              "      <td>13995</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>corolla le</td>\n",
              "      <td>good</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>96586.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108989</th>\n",
              "      <td>18900</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>f150</td>\n",
              "      <td>excellent</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>62500.0</td>\n",
              "      <td>rebuilt</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398128</th>\n",
              "      <td>3988</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>chrysler</td>\n",
              "      <td>town country</td>\n",
              "      <td>good</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>110751.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>compact</td>\n",
              "      <td>van</td>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85359</th>\n",
              "      <td>10990</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>lincoln</td>\n",
              "      <td>navigator luxury 4x4</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>162229.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>SUV</td>\n",
              "      <td>brown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86219</th>\n",
              "      <td>45498</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>corvette stingray</td>\n",
              "      <td>like new</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>18760.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381494</th>\n",
              "      <td>18400</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>silverado</td>\n",
              "      <td>like new</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>15291.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>truck</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159490</th>\n",
              "      <td>13995</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>silverado 2500hd</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>205810.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351277</th>\n",
              "      <td>9200</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>s60</td>\n",
              "      <td>excellent</td>\n",
              "      <td>5 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>82282.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197291</th>\n",
              "      <td>11000</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>dodge</td>\n",
              "      <td>charger r/t</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>106000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>other</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>389447</th>\n",
              "      <td>7995</td>\n",
              "      <td>2004.0</td>\n",
              "      <td>jeep</td>\n",
              "      <td>wrangler</td>\n",
              "      <td>excellent</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>141752.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>4wd</td>\n",
              "      <td>compact</td>\n",
              "      <td>SUV</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355224</th>\n",
              "      <td>8900</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>bmw</td>\n",
              "      <td>x5</td>\n",
              "      <td>like new</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>119000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>SUV</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>543275</th>\n",
              "      <td>28900</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>ram</td>\n",
              "      <td>3500</td>\n",
              "      <td>salvage</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>122726.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>489938</th>\n",
              "      <td>2900</td>\n",
              "      <td>2005.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>matrix</td>\n",
              "      <td>fair</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>256000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>grey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23967</th>\n",
              "      <td>26975</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>sienna</td>\n",
              "      <td>like new</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>40932.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>compact</td>\n",
              "      <td>sedan</td>\n",
              "      <td>grey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136803</th>\n",
              "      <td>29402</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>f-150</td>\n",
              "      <td>like new</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>48513.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148839</th>\n",
              "      <td>9000</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>like new</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>32000.0</td>\n",
              "      <td>rebuilt</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37563</th>\n",
              "      <td>18950</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>highlander limited 4wd</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>92149.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>truck</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113125</th>\n",
              "      <td>2000</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>bmw</td>\n",
              "      <td>525i</td>\n",
              "      <td>good</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>195000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123768</th>\n",
              "      <td>1000</td>\n",
              "      <td>1989.0</td>\n",
              "      <td>jeep</td>\n",
              "      <td>wrangler</td>\n",
              "      <td>fair</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>rebuilt</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>compact</td>\n",
              "      <td>SUV</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315342</th>\n",
              "      <td>750</td>\n",
              "      <td>2003.0</td>\n",
              "      <td>cadillac</td>\n",
              "      <td>seville sts</td>\n",
              "      <td>fair</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>193085.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369593</th>\n",
              "      <td>2300</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>rabbit s</td>\n",
              "      <td>fair</td>\n",
              "      <td>5 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>162000.0</td>\n",
              "      <td>salvage</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>compact</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437473</th>\n",
              "      <td>3500</td>\n",
              "      <td>2005.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>trailblazer ls</td>\n",
              "      <td>excellent</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>163263.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>compact</td>\n",
              "      <td>sedan</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193416</th>\n",
              "      <td>32990</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>ram</td>\n",
              "      <td>3500</td>\n",
              "      <td>good</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>30921.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295244</th>\n",
              "      <td>6300</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>s60 2.5t</td>\n",
              "      <td>excellent</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>98500.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>van</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>304102</th>\n",
              "      <td>15500</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>ram</td>\n",
              "      <td>1500</td>\n",
              "      <td>like new</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>70000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>compact</td>\n",
              "      <td>truck</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213335</th>\n",
              "      <td>4900</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>volvo</td>\n",
              "      <td>s40 2.4i</td>\n",
              "      <td>excellent</td>\n",
              "      <td>5 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361855</th>\n",
              "      <td>2995</td>\n",
              "      <td>2004.0</td>\n",
              "      <td>jeep</td>\n",
              "      <td>grand cherokee</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>261585.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>SUV</td>\n",
              "      <td>grey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145692</th>\n",
              "      <td>24777</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>f-150</td>\n",
              "      <td>like new</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>44341.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>sub-compact</td>\n",
              "      <td>truck</td>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>426655</th>\n",
              "      <td>9486</td>\n",
              "      <td>2011.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>traverse</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>other</td>\n",
              "      <td>109213.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>grey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150158</th>\n",
              "      <td>23991</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>prius</td>\n",
              "      <td>like new</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>hybrid</td>\n",
              "      <td>25655.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163201</th>\n",
              "      <td>32999</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>f-150</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>71010.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>truck</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        price    year manufacturer  ...         size         type paint_color\n",
              "243303   7897  2012.0        honda  ...     mid-size        sedan       white\n",
              "464061   6699  2010.0          bmw  ...    full-size        sedan        blue\n",
              "203706   2950  2010.0     chrysler  ...     mid-size        sedan       black\n",
              "11384    5595  2008.0     chrysler  ...    full-size          van        blue\n",
              "246093   1800  2002.0         ford  ...     mid-size          SUV         red\n",
              "500457   4500  2007.0        mazda  ...      compact    hatchback         red\n",
              "36085   29995  2017.0    chevrolet  ...    full-size       pickup       white\n",
              "345164  27991  2007.0         ford  ...      compact  convertible       black\n",
              "276670   5275  2010.0          kia  ...     mid-size        coupe      silver\n",
              "328190  13995  2017.0       toyota  ...    full-size        sedan       white\n",
              "108989  18900  2015.0         ford  ...     mid-size       pickup      silver\n",
              "398128   3988  2006.0     chrysler  ...      compact          van      silver\n",
              "85359   10990  2007.0      lincoln  ...    full-size          SUV       brown\n",
              "86219   45498  2015.0    chevrolet  ...    full-size       pickup       white\n",
              "381494  18400  2012.0    chevrolet  ...    full-size        truck       white\n",
              "159490  13995  2003.0    chevrolet  ...     mid-size       pickup        blue\n",
              "351277   9200  2012.0        volvo  ...    full-size        sedan      silver\n",
              "197291  11000  2010.0        dodge  ...    full-size        other       black\n",
              "389447   7995  2004.0         jeep  ...      compact          SUV       black\n",
              "355224   8900  2009.0          bmw  ...    full-size          SUV       green\n",
              "543275  28900  2014.0          ram  ...     mid-size       pickup       white\n",
              "489938   2900  2005.0       toyota  ...     mid-size        sedan        grey\n",
              "23967   26975  2015.0       toyota  ...      compact        sedan        grey\n",
              "136803  29402  2017.0         ford  ...    full-size       pickup       black\n",
              "148839   9000  2017.0    chevrolet  ...    full-size        sedan        blue\n",
              "37563   18950  2013.0       toyota  ...    full-size        truck       white\n",
              "113125   2000  2002.0          bmw  ...    full-size        sedan       black\n",
              "123768   1000  1989.0         jeep  ...      compact          SUV        blue\n",
              "315342    750  2003.0     cadillac  ...    full-size        sedan       black\n",
              "369593   2300  2007.0         ford  ...      compact    hatchback       black\n",
              "437473   3500  2005.0    chevrolet  ...      compact        sedan         red\n",
              "193416  32990  2016.0          ram  ...     mid-size       pickup        blue\n",
              "295244   6300  2009.0        volvo  ...    full-size          van       black\n",
              "304102  15500  2014.0          ram  ...      compact        truck       black\n",
              "213335   4900  2008.0        volvo  ...     mid-size        sedan       white\n",
              "361855   2995  2004.0         jeep  ...     mid-size          SUV        grey\n",
              "145692  24777  2017.0         ford  ...  sub-compact        truck      silver\n",
              "426655   9486  2011.0    chevrolet  ...     mid-size        sedan        grey\n",
              "150158  23991  2017.0       toyota  ...    full-size    hatchback       white\n",
              "163201  32999  2015.0         ford  ...    full-size        truck       white\n",
              "\n",
              "[40 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jtXbk30M4gs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5d_09bLM6bX",
        "colab_type": "code",
        "outputId": "76626bef-7abf-47bf-ae63-2f09b6fc1577",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        }
      },
      "source": [
        "sns.pairplot(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f354d4425f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TyhpsDifLt5ay4LZ+PPH3gyH9KELVkcDTz3QaVcS6Kj1bJ392tMrIzNdKSIrT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LijUlBeR+ayLWQ/u41dRRUSW5I3pmcnuhJflSr2LplI/+6RvLP/FM2WKys81+50IBdk3J7ZA0Em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SXa+kGNhzuP1RIE3tlZGVFn57FaFvx8ADuOnNXGYM3+03jaiEceOcCvL+8W1U5ZdVO71yHr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\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Rk1gARUisYIyoWIJK770pdRdY6s72nd1p9955P8zey9TdNUHXJPN8Ueae8j/n7tz/OXPP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78tVWJ/tPVwfocw6VW3jk/a949NouASnFbq+X+UEL92CbTBlyQTtu8TamleQGLDLCWZXWWJ0k\nxenCNgbkULSM5DjS4w1RE4mnlQzl96u+oZmpcZQmGfEGCYNWZNeJC2w6VBlwHi02Ny63l6u6tGBq\ncY5CwzlaUY8g+Fy6rHYXOo0U9RqdPjiXzYcuTlmCu/ny5yTfR/31Ok63l4Qwiyn/9xRv0DLt1R1s\nO1rJ7OHteXt6oW/643Kz4cHBIXkp8usSDFri9FKAJstnG6FChQoVlxbqIqAJwev1Ynf5igejVkIj\nCVRYHLRINPLsLT0VPqv/gyPVrMfqdHNHfltaN4sD4DdXd+IPN3Zl/cFynlx9gGu6tWTzoQplTO3v\nmHHv0HaN4qf3zEyi3u4OsClNMGow6TXY3RdtPWMJIicNyIlaYMsiR3/xnsyFbSz/vjF2p6N7t+HW\nvpnYHC4EUUCvkai2OkkQBHQakZHdWrBqb6izkfz6mhgLjVgc3niDNqrew6CVLqswWcWPg8vtDRsW\nJneX++ck8+j7+5XsgHDWnDIv/kK9g/igyUKN1UmiUYcHD9f3aMX0klzsTg8GnRRQkMvpw7Hsd2XI\n6cNbj1Zi2qxh/h29FO59OKvSv/2yJ1sOHw95n6v2nuHqLr5QtJoGnrw8ffDipV2aWZlWytezVhRD\nBND+8C+25SniwHZp1Fid9MpMoc7uITfNrNzz5vyiA4s2+j6DnplJ9GiTFCLcrXd6Yl6j52sdLN9y\ngj+P7sbVXVvSNiVO0Q0cq6inZYKBUQ36BRny/fCuxVtZPjE/Kk3L6faw63gVaWYDN3RvzekqK5kp\ncVhsLvRmHRnJcfyjtIBRfhSpsQVZWB1uFt7VB6NO4lhFPTpJVC1CVahQcVmgVhRNCBpJwih6ufWl\nzTx23RVIosjKHSfDBhA9/a/vmD44l/R4A3V2F9f1aM2b20/wm3f2cqzCqmz77gxf90kQBLbMHUqC\nUaME7VzdtQVjemfE5KfX2py8Nrk/5bX2gIXINd1asu1oJWXnLJQW52DUSjEFkTMHt6NbmwT2nqwJ\n+ffgjqA/F/aH8O/dnujuJP7FtMcrhe12Pj2mB/X2nXx+4FzI650eT6MWGrFg0EpMHpgToPewu9wY\ntNJlyzJQ8dNg0EbvLk8tzqHcYufA2dqwQVVHK+oVtp6PAuNgxaR8jldaAXC6vXjxcrzCStsU36Je\nDJrURUoflhHOJnPSwGz2nqgC4Lb8DF7belzRIcgWof7f556ZSUx7dUfAfiPZnt43rD3vzigMyLs4\nW2Pjj6u/IT3ewJLxfWgWpyM/OwWA93ZfpDTd0KONkleQm2ZmZWkBSzcd4f+t2q9sc1331kromkYS\nKMpL5d3dp9jw4GDS4w3KZET+mwatRGWdnXiDJuo12syk5UK9AwT4+KvTIfeA8YXZIa+T9To5aWbA\nG9VRTRCgZ0YSUwblIEkCq/adDrvdY9d1ZtqKnYwtyGJ8UTZGnYTN4UUjirRpZlSvdxUqVFw2qIuA\nJoQam5NvTtdQ0j6d9HgD89ceCttxTDXreeTazrywtox135Uz77ZeGHUSgzukM7p3G6VIl7uT44uy\n2HfyAj0ykgG4olUCr03uz/qD5/nj6m/57bVXRH1YmvQaRjy7LuziwosXh8uDAFzTvSX1DndMwet9\nw9tz/5t7Q6hNweN3uWB3OzwR+fdGrcTYorYYtRe/ykadhmlhsgbGFWYxrSRX8eC32Jy8FMWm80+j\nuzHkL2vDFuFWh+uShJrJiwVZBKzTiE0qy0BFIMIJg/1tJ/0nWXJQVX52MrPf3EPZOYsScvXodZ15\nd9f3xBs0eLzwUYQC8cCZWq5olRgwqWuM8NjqcPPxPQOJN/gW/Xcs+pLXJvcnJ81EUV4q/Z9YEyBo\nnfOLjkpXe2C7VKqtoe9zzi86hkz50swGruveKiTvYnxhNk+P6cGZGiuTl+3A5nJxz9D2Fxe9DYuF\nOoeLExV1vD65AKfbwzu7TtG1dSKlxbnKNruOXeBCvYPXpuRTXmtHI4o8Oaobb+04GRLm9fSYHtgc\nLtLMBvZ/XxP1GvV6YfG4vizecCSmRkmGTNcxaUUsNhcGrcTVYSxTPR4vbreXp8Z0xaDRsGBd+Hs5\n+MwVtswdyvqD53l/zylG9WpDQpwOuHg/UCeCKlSouBxQ7yRNCAkGLSu3n+BPo7sjNPhMh0NGspH5\naw+xau8ZVpYWsGzT0YBi158XLPPTX/ziCIvHpTH8mbUYNBqlcEk26Zjzi47Kw1J+4Ds9HrSiyA09\nW/HFd+Ucq/B1KoOFdTNe3UFJh3QG5KXy4e5TTBiYG1Pw2r11EpvmDGFj2Xn+8NG3lFtsijvQmAWb\nQwpuh8sTci78xcd1djeSICoPR5vTzevbjtOtTWJALoLd6Wb1V2e48ormxOk06BpBg9r+0PCwWQVG\nneayhZrFEjarnuE/H8IJg/1tJ+VJlj/tzmJ3UV3vVOguPTOTyM9OoVOLBLSSxKFyCx/tO6NcM/4F\n4rTiHM4HaVQaIzyON2i5UO+bqAn4ePRV9U7G9GpDVX3o5O/5z8tYNq4Pkwb6JnpAwPu8UOcgLd7A\nPW/sCnhdJPqfXHhPHpjNonF9SDXpQSCi+YDg9U1Zru/RitPVNsA3FRGAXm2bhUzFZN/9D/acxmJz\nUW6xB3D27x/eHlESyEs3h4QkypORw+W15DVPaLRGCS7qdaYU59LMpGf7sUq8Hi9tU+KU83a8sp45\nb+9l5dRC/vLJAWYP7xDWPU3+G7OGtONXb+4iK83M9JI8REGgwmIn3qDF4Xbzjx2nFBcidSKoQoWK\nSwl1EdCEYHe5GdWrDSJQ3dBxzEkzBfBs/akAi8f2ZdmmoyEd42BecJXVQXmtw0cb6t464IFdWefA\n5fYwrTiHX3RpQduUOGxODya9Rhmzf3mkIoCTC75idFpJLi/e2RdJFKixOpkwMBe7M7rg9bzFRuEf\nP1ceZqvuHoAoCNhdbvQaiTen9g8puIOFvpHEx3IOwBvbTvDo+/uBwG7t7fmZ9MhI8stRaJxNZ6Ss\nAr1WuiShZsGIJWxWPcN/PsSynfR6vXx09wASjFqFdvfa5P4kxmkDQvX87XEjiXnlAjHVLAYU/D8k\nmTY4hOqWvhmYgxYy9XYX8XoNXkHgpYYifcHtvQLeZ8sEIwvH9gmYDsSyJZUXrCu3H+aWvpm8vu14\nVPOBOJ2EJAoBU5FI2gc5/2TZ+L5cqHcGLMRmDm7nE+9rtdRY7QxqlxbQqZcnI69P6c/52ugmAPJk\nR3YHml6Sx1P//JbFG476DAWGt+fGnm0QELA63Rh1EgJwQ4/WONxu32S0Edfz0zf3xOZy89G+0zz6\nwdchi52/bz5GjdWiTgRVqFBxSaHeQZoQ9BqJXm2bYbG7SDBo2fjgYNKCOK8vfXGICouDknbpdGuT\nxIRl24BQaoLcxRrcIZVEo5b0BB0Grcj4Ih/PVX7I3tKnNXE6DU63hy1HKkhPMLBk45GwOgT/IqXG\n6uK8xc5n356ldVKcUnDMHt4+rOBVLtDxwkt39ubJ1QeUh9mUQTlIohAxHCyYfx+r+9itTaLye3/r\nPtmtxGJzkmzWXxKbTvkh/GNDzcLhUugNVFwexBIGTx6Yjcvjc3KJ00n85eYeSKLA02O60yxOy0vr\nD0ekhQRTT+QC8cSF+hBai2wbGhys5Z9M679/QYCb+2Qw4MnPeX1KPq9NzqfO7rPD9SIgiCKHyy2s\n2uubSHi8BNhrakTf9MF/8VBZ54hpS2qxOSktyUESYk/dnG5PAG0m2aSjICc1ovZhyaYjlBbncv+b\nezhba+W67q1ZPLYv9Xbfe/7swFmu79Ga6SV5/HrlXg6crQ0wNrjQMCGMdq2lxxtYMTmf+IZ8lo/2\nnWZAXhobD1ZgNmi4tlsrFm04HHa6IQoCpcU5AbkP4f5GvEHLnpMXWPddaHJxJFqSOhFUoULFpYC6\nCGhCqLc5iTNoMWgk3F4vb2w/EfJweWhkZyTg8Ru6UNNgz/n0mMBEzF3HLuAFNKLIH2/qjoDAC7f3\n5pvva3jgH/t48KoODTaALsx6iSPn61i17zTd2iSyZGP4AgcCH0SyiHfRhiN8fM8gZWQud+Bl7m+t\nzYlJr2Fj2XlG/nUD5RZbCF1panEOf990jPFFWWG76P5C31hprEs2HQkZ4ctINeupt7tINPr4tk53\n9KmF42ey6WyMsPlSLDZU/HA0Rhh83fMbcTV8hpMGJnHzi5s5UWllw4ODo9ppBn9v5fRbu9MT4uFf\nbrHx/p7vQ4Tl/sm0/li66SjTin0hVPF6DaIg8sqWwOJVvi6nvLKDXm2bMX3FTuBig2HfyeqIU5A3\np/bn/pV70EsiB8stCAhKKriviSEpiwU5s8RfS1HvcBGn0yi0mWSTjr4NDZFoXfR6h4u//rJHgHYg\nM9nExIHZPHBVBzYcPM/r247zwFUdGPjU58prfbbJOrYcrog4URlfmM2eE1UsXH9YmcTKIWDdJ/VD\nr5WiaorkRdfmOUNi2gF3bpXIhAg5CuG+G+pEUIUKFZcC6iKgCcFk0FLvcCMIsHDD4QA3kDSznuVb\njiMgMKHBQQIXvDO9gJfXH1Eeyt0zElk+MZ+F648wfcXOgAVEabGPw/6rt/ZQOiiXcUVt0YgSmSlx\nvL/nFNNKcrl/5Z6QqQKEPohkEe+xCisX6pykNTyM0sx6lmw8wm392mJ3uli173ue/bQsoCAPXlRU\n1jl4Z9cprE532BG3P/9+Q1l5TFGkvzgTYHCHdH5/wxWKk5LD7cELOFxeHx85TOiQbNP5c+By6g1U\n/DTEonbI370DZ2t5bs1BPF4vM0ryePpf3/3g760cfNUzsxljXtykePgH01pWTi3kk6/PMrhjWtRA\nrgv1dgZ3SCczxRQ2F0S+Lu8b1p4KiyOgwVBZ5yDJqOXlKOFb827rRb3DTaLR53L1j50nFQ2S7AT0\nwcwiBAHOB2kpTDoN1UFNjZqGCWisMK9pS3ai1/gaHQvXHwnRR40vysas1/D/2Tvv8Kjq9O1/ps9k\nJr1Q0xODSCeQCiQUaRZWRVZFCb0GRNcFF9fX1UXFd13FFUQ6KxbAriirIj0BQleRQEKoUlIgySTT\nZ94/Jucwk5lMQvF1fz/nvi6uiyQnZ74zOed8n3Lf95MQqRUHLQrPrwWbivlwSgZSCaxqVHCZ2MeZ\nBPzn6CXg2nMEwI4EpVxG1/YhHlRJcLdWDglQkNcgRG7cYc3LjEcll3GlvmW0JF9ThP0CYj/88ON6\n4X9C/BfBZHVySsG5iTSevBuuU1JYWolSLmHQa1vFVveP52vEDWRGbhJLt3tSDgQO7SfTMpHLJJy4\nWIsEMJpt1JosJEcFYTDbeHVkVw/Bo9Dqr6ozEx8RwOi0GPKyrol4IwNV/O3ejnRpHyL+nsFsI0Ap\n5x/fHPe6uQlJRWy4RhRT+mpxu/LvAZ+BQURDAgPOjfut0T1EJ6WVY3qxZFupx1RUV16/ucGm82Zx\nM5vyr6U38OPm0By1o/HQK+E6f3nTsWbFvK6iYqdQPo6/fvYTT955m5uHf+MEvdZooX+HSEK13qkt\niZE65g3vQIROzVODb/M5F8RJmUtEAnw0NYOVu5wFBrlUys45uc12QQa/vh2r3S5y5UelxlChv2Yt\nvH7/WaJDA9z0CmvHp3HsYjUdWgfz8dQMVuy6VtRYmZfqtYqeGKnjzYe7i1SrqCAVJ8vr+PLIBa8C\n6wnZCfRLjsRmt3Nvt3ZulCmpBIZ0asPkfo0cfhwOesSGEqZV0rV9iPgc8dY9mfjv/dSZrG7djRqj\nhW7tQ6jQm8lbVSR2YF2TuJFLClk/OZ2wAN+0pMbXVeOOoN9S2A8//LgR+JOA/yIo5TKsdhv1JjuR\nOrVX8euYjDgyEyNIjgoUN8bn772DPq9cbhFVZnpuEmUVepJaBSKTSqmzW4gMVLPwj12RSaUcOVft\ntdU/bk0RUYFq3p2QjtVmRy6VMGdICuevGjBZbRSUVjLpnf0eGoBInZoag/dpw1V1ZsZlxbvZgvpq\ncQsBdEvsOR9Nj2VVQRl/H3GHWPVc9lhPDy3B4bPVDF24gzlDUngsIxalXHpLKEC3YlP+NfQGftwc\nDBabzwFRrtcyXLvOFVIpB05f8Xnd2u0OvszPJkKnYseJcnGg2Pw/dBIDRFeNCzgDxAClnBnvH+D1\nUd08AmZBRL+m0ClGbizwbQzhNVQyKWv3XBPyprQKbHEnw2p3eOXKiz74CqnYURS6CJP6JlBttKBT\nyenUNlh8bszfeIz1k9OBa9oH125nU65o3gTWM/on8fSw2zFZbWz++TJWu41/juzGyl2n3JzRhEB+\n1oBkxmbFEalT8YLLc8T1PX/1w0WGdW7D+5PSuFxjcutuBKkVVNWbG4oSRq9JnFDRN1ltTV4beZlx\nbsMeGz9H/JbCfvjhx43C/2T4L0K92YpSLiVIo2DecE9PbtfW+8NpsXz382Wx7ZwQqUUhlTa7Udca\nLaLFqGs1fO34NJY2MZcA4OX7urC3rIqZHxzk0fRYRqa255dqAw/1juGrHy64bY5yqZRNP15Eo5Dx\nl2EdvFIUhIr93V3aMvLt3YRplcRHBLRI9NoSusyUfglM6puAQiZldcGpZhOkt7aVMj47odnXbgn8\nm/L/XmgUMg9xfeMp3K4QqrjBGgU6tcKD2+9qWSmRglwqwWKzc+RctRjI+nICGpsZj81u54V7O6GS\nyzxE+fOGd2BN4Snx/hQEvr6qzuE6FbJGFsUtsSUVqtUL7u/c5LMLYFxWvBu1Regi3PnadnG42YeT\n03l9cwk7SsrFoWv75g2iss5EkFrB29tLm6QzzRt+Owu+PuZWla81Wli5s4wdJeXkpEQxLSeJYZ3b\nOD/DBnOFxgmWUDTpGRtKVKDao3vS1PA0obthtduZO6QDNsc1jU/j1xA0AUqZlEl9E3Dg8DBUmNLP\n+VzaOSdX7FJIXNbhtxT2ww8/bhS/aWnxpZdeolevXgQGBhIVFcWIESMoLi52O8ZoNDJ9+nTCw8PR\n6XTcf//9XLp0ye2YM2fOMHz4cAICAoiKiuKpp57CanXfqLZu3UqPHj1QqVQkJSWxevVqj/UsWrSI\nuLg41Go1aWlp7N2797rXcjPQKuXIpVLqTRayEiOa9JZeVVBGekIYYVql2HZOjtS5bdTeIAz+kkqk\npMeHEalTAzB7YDIqedOCxzWFTmrS3jJnNWpVQRkROhX3dGnLuqKzBGucgXtipI5lj/Vk55xclo9J\n5bHMWNISwugZGwo4tQ0prQIJ0yoZmxlPea2JJzccYe7QFHbOyWXRwz0AZzLUHAS6zL55g9j/zED2\nzRvE5H4JYnVMKpHw0YFzXKoxUmOwtmjAUo3R0uzrtgTNbcqCRemvCYPZitlqp1Jvwmy1t+gz9aN5\nmKw2DpyuYlxWPPvmDWJfw7U3rHMbr4LcsZlx2O0OXhvVlY5tghi9Yg+d2znnV+yck8vupwfQqV0w\no1fsQSGVIZOCVOJgak4iswYkE6SR8/LXxYzPjhe/Bue9PGtAMhP6xPPIij1kvvw9O0vK+aDo2iTg\nwqf7k50U6Ra8utqLeoPQkWisfaiqM4udDG8QuiAAWUlNP7vWFJ5Cq5JjsdvF77l2EXrGhDGqVzRa\ntYL8/klsmtWXd8b3JizA+YyRSxET+6bOn50Uwcq8VHbOyWXpoz3pGh1MoFpB/oBEPpySxcie0Wz6\n8SJWW8vsO/8ytINXO2FXl7LGFKTlO8o4dPYqGS9/zyPL9zChj+ffL79/kqgJ2HrsEuv3nWV8tvOZ\ntmtOf4rmDWRwp9bc9a9dZC3YwvjV+8hesIWhC3ewaGupeE+3xFLYDz/88MMbftNy5LZt25g+fTq9\nevXCarXyl7/8hTvvvJOjR4+i1WoBmD17Nhs3bmTDhg0EBwczY8YM7rvvPnbt2gWAzWZj+PDhtG7d\nmoKCAi5cuMBjjz2GQqHgxRdfBKCsrIzhw4czZcoU3n33XTZv3syECRNo06YNgwcPBmDdunU88cQT\nLFmyhLS0NF5//XUGDx5McXExUVFRLVrLzcLmsGGyOJwiuWZcMVz9q4PUCk40cPZ9VQ3HZMSx/Xg5\nP5yvZljnNqITSFpCeIs2w/z+yeRlxfOP/xRyjKpIAAAgAElEQVRToTex+edL3NYqkK7RIW4e6I29\n+/89rjc/nL8qagYiA1VYbHZe++44/xjZxevvtIQ2Y7M7kEudAb/wNVyrxL+z+zQP9GxPkEbeokpm\nSyxBW4Lf2uffzw/+9RCoVpCdHMnirSV8dug8armcfz3UjfgILcO7tBa/JwhhhSBdJZex+JEezXL7\nAWRSGRabjbu7tmFKvwT0JisahYwhnVozpV8CVfUWInRKTpbXMWJRAaXlerf5ITUG5yTg3A5RPD20\ng8e1KNiLgns3w+lmFM/8jT/x3D2dPe4VqYQmOxlCF0So8Pu6/qsNTnqUAKGLkNI6kFce6OL22Rqt\nVlH7NHv9QUou1fHOhN4+z3+51sj41fu4UGNgTEYcH0x0vle90U6QRkawRsGwTq1x4GiRfedTHx7m\n/z7Q1e24llAvJ/dLIEyr5PDZakYsKuDNh7szLTeR8lqThyYgpU0gk989SFWdmUnZCew5WcGQzm34\n49JC8TW9dSmEa9JvKeyHH37cCK47CbBYLEyePJm//vWvxMd7rwq1FJs2bXL7evXq1URFRbF//376\n9u1LdXU1K1as4L333qN///4ArFq1ittvv53du3eTnp7ON998w9GjR/nuu+9o1aoV3bp144UXXmDO\nnDk899xzKJVKlixZQnx8PK+++ioAt99+Ozt37uS1114Tk4B//vOfTJw4kbFjxwKwZMkSNm7cyMqV\nK5k7d26L1nKzsNpAKpVgstrRqeQtar3nZcZRY7RQ0SAae3NLCWvHpyGVSDz4ssJGXa43MjUnkdW7\nTjF74G3UGCyENiNMC1QrmPROEZ3bh/Ds3R2ROBys3FXGt7NzAPjXQ935/PAvTXr3D+3UmvSXNrsF\nHH8e3IF1RWeb/J0JfeKb3MCMFhvLdnj35xYq8TUGq1tS1NyAJVMjS9AbFfb+lpuyn4r066LWaGFZ\ngy1kmFZJqygNsRFaFmw6Rn5ustOu02AhSOO0xpz5/iEOn62md1yYW8DpjdsvTPm12u2cqTLQJkjN\nI8v3sjIvlfeLztAuRENseAByCUiQMMolQGzc6aqqM7Pl2GVedNETCCgt14sUm/z+gyivNREZqKK0\nXM+IRQU4cGC12ZjSN5FX/uPszIZplXSLCeWhZbvdXIqiAtXi+YRkJDLQ97MkSKNwE7kKXYQ/D0nh\ng6IzdG4XzJR+iW7mBB8UnWHukNu5d9GuZh2DhGejUJWXSKBr+xBRjHxtSrEDs823xsNstXGh2kh5\nrdFNb9GSzqKro09puZ5RSwvZ/fQAnlh/mJLLejdNwC9VenGatEopIzs50m0GgzdbVaGY4LcU9sMP\nP24U1/1kUCgUfPTRR7/GWqiurgYgLCwMgP3792OxWBg4cKB4TIcOHYiJiaGwsBCAwsJCOnfuTKtW\nrcRjBg8eTE1NDT/99JN4jOs5hGOEc5jNZvbv3+92jFQqZeDAgeIxLVlLY5hMJmpqatz++YJSLkMl\nl6FUyCgo9d2yP3T2KqPTY5iWk0SAUkrRvIHs+csANkzO5GK1gbFZcR6UA2GjFjaozccu0SM2hCCN\n0xGnqdcTNsPD52p4Y3MJi7eWYLQ5OF1p4HKtkYeWFbKu6Cz3dmtHYqTO4/dXFZQREx7gMqnXGZS+\ntbWU9qEar6+5qqAMhUyK0WLz+Fmt0SKK9Fzb8MLaDGab+P2Xvy4mLzOO/P5JvLmlRPy/a1t+5oAk\npuUkuQXnQjU9df639Pz7d6TO/5a3t53E5GU9jSFsyt4gbMq/Fv4bqEj/k3C996hKLmPb8XKR9vba\ng92QIOFPg1JYVVBG6vxvSZ3vvF7W7j7NPx/sSmKkDo1SisVm93mPWWx2Fn5XwtCF2/nqhwvIZBL+\nz10dCFDKuadrO46cqyb9pc08trJIpLkJ8EYFrKozU1RW5fVaLC3Xc+jsVXaVlLN4qzN4nPuxk5r3\nZX42epONCX0S+HpWH7pGB4tBr9DJyG6gp/xp/WGxCyK8tsXazPu0Oq//a5SYON7bc4aoQLXb++zz\nyhbSX9rMD+eruadrO1oFqYkND2j2/I3F2asLTpGRGC7SJ4XnhNkOJeV6JvVN8Hgm5PdPYlpOIkq5\njFdHdiVCq2JaThIzByR5dBa9wZujj/DcDdVce86MyYjDYLYRGqjmwykZHDp7ldT539L7xc2kzv+W\nn36p4Yv8LHY1UCwbU5zgmkbKG11sWk7i//ik/3rvUT/88KPluKGnw4gRI/j000+ZPXv2LVuI3W7n\n8ccfJysri06dOgFw8eJFlEolISEhbse2atWKixcvise4JgDCz4Wf+TqmpqYGg8HAlStXsNlsXo85\nduxYi9fSGC+99BJ/+9vfWvwZGM02FHKoM9ndXDEat96n5iQik0iIDg3gyQ2HeO3B7oCTK/un9YfZ\nWVrBzjm5DF24HbVc7kY5gAbxn1ZFfv8k5FIpVw1mQjRKUWzb+PUm9U3g8Nmr4jkEMXJ2knNjLauo\nZ/9ppyd648mW4N3nGpxB6Z6nB3od7FVjuDaR+L7u7dG5BOi+LA6FtcWGazhdaRCrlHOGpDA1JxG9\n0cqUfonk908WK/z1ZisOhx1omdvGpL4JSCQ0ubn+lj7/vzUV6X8arvcerTNZWTmmF6sLTvH3jUdR\ny+X8fcQd7Cyp9BCqCtfLc/d05PufL2N3OJjU1yny9HaPOezO+0chv52dJyr4cP85/ti7PQaLzW2I\nnzdxb1NUwP/z+VE+mpoB4LUzOPHf+xmV2t7tfTWm5q2blIHeZCFIrfToZBRfqqXWZOWFEXcwIzeZ\nOrMVi81BXmac1/eZlxmH1eZg85P90Crl4v2ZFKl1e5+CfqhcbxLfz8TsBO68vRW1RqvP8zcWZ3t7\n/gjPidMVdcSFaRnWuQ1Tc5wWoeFaJ81y/b5zvPptsWhVeqaqXtRbVNWZsdsdTQ4B85aMCBOIn727\nI6ENAt/2IWrUChlGs1MQ7O0acuBw62QIwmOb3Y5Qx/vfbCl8vfeoH3740XLcUBKQnJzM888/z65d\nu+jZs6fI3xcwc+bM6z7n9OnT+fHHH9m5c+eNLOm/Ek8//TRPPPGE+HVNTQ3R0dFNHq9RynAAQRoZ\n5XqjGLy6eksXllYilUiYvf4gXx5xJh/P3+OcFvrSV0f5/MgvgNNR5J6u7Xy0ue3sP32VmR8cEjfR\nJwelMKlvAtNykqisM13zy7Y7eLZhEjBcE9GOy3bf6LxNtgTvVTHhPPVmKz1iQvnuZ3eBtetE4j/2\nivWg5ght8sZCTCHQHZ+dwLOfOTtBpeV6kYf9+MAk4sK1vPbdCTISwmgfquG+HtFucwGaq6Y3N+EY\nfrtN2c8Pvj5c7z2qlEtZv+8sXdoHMzXHSVlpFaSmxmj1OjRqVUEZ03ITWVd0BpVcRr3Z6hZwhmmV\n/FJtxOFwXjOVehMhcinRYQH0jg9DJpERoIDPD58Xz9k44Bf0BUu2nWTpoz2RSGBr8WXaBGmQyUAp\nl/BoeizTc5PEycL7T1fx84Ua1k7oTa3BjFIuZck2T3ewhZtPoFHIeCQtBovNO+1kS/FlNuwLYmKf\nBOx2O4FqZZPDzcatKeKDiRmMWbmX+MgA/nZPJ4Z3ac2B01fQKGRsP+HssjSegC6VSlArZYzo3o5w\nncrj/FGB6ianJTdVla8xWogKVHP/kkLmDEkhNjwAnUqOzW4XKV8JkVoGdWztobeI1KnQqeUsfbSn\nxxCwvMw4tzkEAvIy49hxopzxa/bRNTqYBfd1QaVwDkkL1QXQpX1wk4PHXJ+rgkPc5H7ubmb/Wy2F\nr/ce9cMPP1qOG0oCVqxYQUhICPv372f/fveqr0Qiue4kYMaMGXz55Zds376d9u3bi99v3bo1ZrOZ\nq1evulXgL126ROvWrcVjGrv4CI49rsc0dvG5dOkSQUFBaDQaZDIZMpnM6zGu52huLY2hUqlQqVpe\ndbXYbNRb7MikErHC1FhEODo9hl5xYfx8wblRCIGdwWRl/h+68Ne77uCTg+dF8Z83bcCUfoms3+fJ\nxf8/n//kFKb1TUCnkiOTSiivNTHjvYNuG5Mgou3RPpT7XTa6pir+YzPjqdSbCQ3wTA60KjmPpEV7\nJAFjM+PZf/oKcqkMu8PhIXRtyhM8SCNHp5LzYGo0lXqz2+88mh7Lg6kxSCUSlj0WTFATg8Gaq6Y3\nN+FYwG+xKfv5wdeH671HFTIp93ZrxyqXgVau1+Okd/ajN7oPjdIbrcSG6zBZbHx66Dxtgxu4/VIJ\nEqBNkJq3t5c2aQ1ZbbDw9ay+boP7Xv66mI+mZDCscxtiwwPEgPlqvZmJfRJEbUKASobN7hTyfn7Y\nKbZdO74XndoFs3T7Saa9dwC5VMrupwd4OPq4DiusN9vQyuRM6BNPmFYpVsiFtU5sEEAfPlvNrjm5\n9Lst0qsAeuaAJCr0JvaeqmLvqSou15h55YEu5Ocmo/fSjXCdCTB57X6xKt/4/E8PS+HQ2aseATQ0\nXZUPUis4cbmWK/VmJr2zn7lDOtDvtkhiIwIoLdezc04uUYFqqg0W5FIpG/P78OxnP7Gl+LJ4LqFQ\ns2/eIGcCF6DEbLXx8cHzlOuN4mu5JgaJkTrxfTbuZHh7pnl7rv6erD+v9x71ww8/Wo4bSgLKyrxX\nSa8XDoeD/Px8PvnkE7Zu3eohNO7ZsycKhYLNmzdz//33A1BcXMyZM2fIyHC2uDMyMpg/fz6XL18W\nXXy+/fZbgoKC6Nixo3jMV1995Xbub7/9VjyHUqmkZ8+ebN68mREjRgBOetLmzZuZMWNGi9dy05+H\n3TmRtLS81s3vu6rOjNVuFwVtO0+UM2dICnM//oFZA5Iwmm38eKGGKe/uF48JVst5b88ZxmfHMz03\nSRSh7S6tRCqB1747Lr6u60a9qqDMOTFUImHlzjIWbCr2WGdeZhwGi437XTYrwec/QqfC2GDPKmxs\nYzLj+OzQeY8NbkxGHLtKKshKihDpO0EaOXkNvunHL+p57p6OvLW11IOaIyQwjelHYzPj2X/mCqmx\nYV4r8ULAHyEE5l4GgzVXTW/JhOPfCr8lFen3gMbUHHCd3wFrxvbiSr3FbWhUoFrB6LQYVAoZf+wV\nw+KtJcz/ykkleuau2yk6VdViGohwDwE4gK9/vCAmD8O7tObVkd3EqbZyqZTtT+W4TRAP0yrRKOUs\n23HtPTgHgZncrndXD/zG9KAp/RJ4MLU9lS7Tda02B3qjjTCtkjCtkrzMeLFCXnyp1s0S03VY2Jmq\nen44f5XspEiv3QjXCegCRejN70tZ9lhPwPl8LL5Uy2vfnfBqiOCrKm+y2sjt0IoHUqM5U1lPuxA1\ns9cf4aU/dBI/R6GjcqHGQE7D9PGpaw+wpfgy4Owy/unDw+ybN4hnPvuRg2euEhqg5M2Hu7Nv3iCq\n6kyEaVVil6J9qJp5wzr6nKPQ+JnWVCfDT+3zww8/bhY3pRgym82UlZWRmJiIXH79p5o+fTrvvfce\nn332GYGBgSK3Pjg4GI1GQ3BwMOPHj+eJJ54gLCyMoKAg8vPzycjIEN147rzzTjp27Mijjz7KK6+8\nwsWLF3nmmWeYPn26WD2YMmUKb775Jn/+858ZN24c33//PevXr2fjxo3iWp544gnGjBlDamoqvXv3\n5vXXX6eurk50C2rJWm4WSoUMs81OfIQOicPB2Mx4Z0WvoYVvstiQ4gxc0xPCKZjbH73Jilopo0v7\nYLcpwhP7JGCy2lHKpRw8U4VcJiVIE0THtkHUNQhnXSt9rk4cdWYrO46X82BqNPVmm0ere1pOEs9/\ncZTScr3bOWoMFqQSCRtn9kFvtBKkUYg2eKUNFqZzhqTwpw8Pu/F335+UxqbH+1Kpd7b160xW5FIJ\nHdoEopBJmf7eAa+flyv9SBgyNKFPPEfOXqXGaBED/eutxPuqpjeuKv43bsT/m/nBvzU0CpmP+R2n\nmNwvkXsX7RKvx7Xj0zh87grPfPITRquVxwfexoRsZ6Ve+Ns0dX17o4GAM0iUSGBlo2Tk6aG3u021\n7R0XhlLuvt6kSB3KRjNByvUmggPcE19XD3wBQnJidzjoHhPqNpArv38Sc4ak8Oo3x6nQm8lbVeRB\nZRSeBWvG9SJSpyI0QCkmGn/74ie+md3Pba2CDeebW0o8KEL7T18hIyGcGbnJXK41Onn8Dbaqk/sl\nuHH7Pz5wHqvdRkqrQDfL0Sc3HGLjkYticjM+O56MhDDUShlf/3iBB1OjmZGbTLXBQrBGwaUaI1//\neEGc0C4gLzOOOpOVzT9fJkgjZ3R6jGh4oJI777nYMA2fTMsUixC+5ig0plQ21cnwU/v88MOPm8UN\nJQH19fXk5+ezZs0aAI4fP05CQgL5+fm0a9eOuXPntug8b731FgA5OTlu31+1ahV5eXkAvPbaa0il\nUu6//35MJhODBw9m8eLF4rEymYwvv/ySqVOnkpGRgVarZcyYMTz//PPiMfHx8WzcuJHZs2ezcOFC\n2rdvz/Lly0V7UIBRo0ZRXl7Os88+y8WLF+nWrRubNm1yEws3t5abhdVmw2YDs82O3mhl1geHWJPX\nixOXaglQykhuEMqlxYdjttlZ+N0Jlu0oIzZcw/jsBFaMSWXc6n3sKqlgRm4y9y7aRYXeJAbco5fv\nRS6VsHpcL7pGB3sVAo7JiKPvbZF8fOAc//q+tElNQvtQjc9zjMuO59EVe9l/+or4/tYUnmLfvEHs\nfnoAO05UiHalOpWc4W/sJC8zlrhwLY+tLCJII+dPd6YwrHMbn9Qcg9nGVzOzxYRj9Io9vD8x/aZc\ncJqqpjcWHv43b8T/W/nBvzWam6ch0DaKL9WK3O2JfeJZPiZVTLI/OnCO5CgthSevMDYrrsU2k3At\nSATcPOoTIrVuU23DtEo6tAn0oLYFauReB4E11hj48sBfU3iKqTmJboYDwrpe3nSMcJ2Scr3RKx1I\nMCVoPFm4d1wYV+rMbvdtpE5Fdb1FfMb86/sTYlW+321R5GXGUV1v5oUvjlJ0+gqhAUr+MqwDMWFa\n5FIJDpxDz+7r3o6HeseIxRSD2cbzXxylsLRKFB8LCf+4LOfwrqGd2ogdlcYFEKlEQkKk1uXZGo9W\nJadgbi5hASqQ4Pa7fx5yG+OyEli81WlT/Pqo7i2eAdNUJ8Mbte9GLY398MOP3y9u6Anx9NNPc/jw\nYbZu3cqQIUPE7w8cOJDnnnuuxUmAw+Fo9hi1Ws2iRYtYtGhRk8fExsZ60H0aIycnh4MHD/o8ZsaM\nGSL950bXcjOQy2TIZc42v0Iq4YNJaRgsNnrGhlFtcLpRRASqqKh1VsNmDUhmWKfWpLQJoqrOTIBS\nzr/H96K2YYNZOKobj687LFbzpvRLYNI7+9lVUsHL93VpsiUtlUgYnR7HpHf2e93Id83pT7foEB7L\niHWjFbieA2By3wS3tnaNwcmVnr3uEHvLqgCYNSCZQ2evcneXtoxMjeGFL46Kx77+3QkeTI32Sc3R\nKGVMXXmAkoZOg/C7gWqZV6pPS+FaTReCFyFxEehMfo797w/NDZdqTNsQhOSu3YGxWU5KzKqC09d9\nPqeY3oZMKnH7neRIHdUGC5E6Na+OdHbmrtQ5g8EVY1J58atjlJbrnfNHvLym6wCxgtKK6/LAd/2e\nQip1Sygaz0PIy4wTZ5pkJ0W6VfnrzTZ2zskVtQ/lehOBajnfHL3EqF7R5Pd3r8pvO17OnXe0IilK\nx2W9iYNnrjJ+zT4OPDMQTcP09dAAJUu2eeotnhl+O3+963YqXDqgi7aWMDUnEaPFxvKdJz0oWsLX\nE7ITWD8pnQCVXOxurJuczrLtZUzoE8+6fWfFORIprQIZnRYndmjCtMpmhxZGBap5b2IagQ2apY8O\nuOsLvFH7/AMC/fDDjxvBDSUBn376KevWrSM9PR1Jw7RWgDvuuIPS0tJbtrjfG6wOG3YbyGRSPjxw\njnu6tmN1QRnbjpezckwv3t5+0is158kNhygsrSI+IoABHVoxqlc0I98uIOe2KJFD7NpmfmvrST6Y\nlO6D1lDm1pJ23ciDNHJCtQoeXfkjX+ZnN+mi462tHaSRExqgoOSyXqysT+gTj1QCBrONdUVnePZu\np47jvb1nqKozU1Ba4ZOac7Jcz8z+SWwpvsSKXafFwOlmEgABQhUtLEDByl2nWLytxL/B/s5hsPge\nLtWYtuGtOwDOinOtwSrO52jp+QThu8OBWyB5olxPsEbBhikZHqLlvMxrWgK5ROLV5Uew6lz0cHfy\n+yeLr9XS5MT1e0JCIZHgFnwLVXONQkpSlI56s3dbUleRtVIuZVhn31X5sVnxzBp4G3qTlTe3nECn\nVrB0+0ke6h3NvwtPN2ndmpYQxsPL9oivuXJML0xmGxqVvFkL4vf2nGbpjjKxQBCkVnCyQk9koFqc\nI5GVFIHeaEatuGZp3NxUd0GrYLHZsdrtXK41cV8PZyejKWqff0CgH374caO4oSdDeXm5KMJ1RV1d\nnVtS4Mf1QSaRYZfYeWtrKZ3aBYkCxGWP9fRatX9jcwkSJLz0hy4oZFIqGgbYnK6s5893pvDIir04\nHA7mDb+dcauLxGCkzmQVnUu8ocZgpcZg9XD5gWuBSVZiBHUm23VVC8dmxmN3OPgyP5uQAHf6zlMf\nHhGTjblDO3DsYi0Hzlxh/sZjfJmfDXgKXafmJCKVSKgzW+mdEM6sgSlcrjHe8uq8UiHjscxYxmXH\ne2zE/hb87wsahYyxWU4Dg5Z41HsLmNcUniK/fzKBGjll5Xqm5yZ5BMy+aCD1ZhtalYx1kzKY8d5B\nrtQ7K/B1JiurCzw7c0IQ/JdhHZj/1c9IJDCxYV6B6z11V5c2RIcFYLXbsNposS4GnMFrYWmlaGKw\n7Xi5qH0QaDiXaow8uf4w/xjZhcUPd0etkHm1JRW+nj3wNowWu8+q/IOp0WQv2CJ+Zn8e3AG73c4D\nPdsSqFb4tPqdlpvoobeYmpNAjcE35avGaKF1sFp8/87uhpGECJ3HvIWpfRMZmRrdZNfF8xpy0pFc\nqZyT+iZgtdubpPY1Z2n832Ze4Icffvz34IaildTUVDZu3Eh+fj6AGPgvX778ljnl/B5htDjtKj89\ndI7J/fry5IbDzfJzVxWUMS0nkTtf3ya664zJiGN6bhI9Y0NZXXiKff2T+fe4XoRrlRitVv7YK4Yw\nre+WdJhWyUO9o/nnd8c9Ap1XvzkuVuxjwzUeA8kEpyAh+BE26Kk5ieAAaUOeKAH0RptbsiBU2t6f\nlMb+U1d4a1sJ4CAvK85N6Gq22fjlqoHWwWr0RisquZRfrhqIj9CKVbJbGaB749jfiha8P4n4nwWD\nxcZnh87TuV2wm0e9UElvbFHZOGAWhPQAf7vnDiIDVZyrMpAWF8aUfteGVZmtNj455MVmMjOOkUsK\nKdcbGZsZz5f5WYCzwqxtpoK9b94g3psQBDhQSKUM6dRaFNEKLj9yqQSHA9QKKWOz4nDg8KDSTOgT\nz+gVe8R1jcmIY3LfRKQSRFtNi83Okm2loi2p0Wrlnq7tePXBrgQoZBSVVZCWGOlTICt0JJqryidE\najlZXudG11m89SQzByT7DOb1RitJUTqRmii8ZpBG5vPZGKRWcLnWRGy4hrFZ8TzQsz1KmYzp/ZPQ\nqpwD0L764SI1Biu7yyoZ3yfB7XyuAwx3Pz0Ag9lGgFLO9hPlIrUoOVLHyfI6Fypnotf3Af4BgX74\n4ceN44aijRdffJGhQ4dy9OhRrFYrCxcu5OjRoxQUFLBt27ZbvcbfDTQKGdVGC2q5XOTkOu37fFft\ny/Um1A3uTK68/tkDb2P0ij1crjVyqrKO3vHhfPN4P0xWO8UXa3z6yRstNh5Oi2VkajRGi41AtULc\nvNaM68UHRWcYeHsr/vN4P8prTaJjh0wC3WJCqWnw1t78RD+0Kjlmq433957htUZJxYYpGWgUUrFS\nGqlTY7DYUMmlJLfSsWJMbyr1zsFl+09Xkhobjtlmw+GATw6e9zp51Wq3I5dKf1WO7K1owft5vP/z\noFHIxDkB8786SqsgDWvG9qJVkJphnVv77A40ZbspHPfoir3ijAFB5Lpv3iBqjBZ0KjnbjpeLTluA\naCOakRDGNz9dZkKf+GaCXgsBSjk2G7y902m721jvM2tAMpP6JmBpmGkgTMi9Um8hNEDBvlNXKK81\nsWFypljhLy3Xc8+bu7hS70zmV+alsq7RHBKAr364yPDObUiI1NGpfWizImujxYbZam+2Ki8EzHAt\nMejSLqhZvUWgWuEUYLmcr9ZoQSmXNjkJWKDr3Ne9PXkNnc23tpY2OcNk2/EKTBabx/mEAYZzhqQw\nOj2WrAXfu1GLTrgkk64JkTf4BwT64YcfN4ob4kxkZ2dz6NAhrFYrnTt35ptvviEqKorCwkJ69ux5\nq9f4u4HRbCVIrcBotYrisfIGik+QxntA2dQ03lUFZfSIDSE2XEOYVsnr35Xw1tZSqurN/GFxAbVG\nCxP6xJPfP0k8t+DlPaFPPD+cu0rq/G95e3up0/mitJyECC0TsuOIClRzT9d2rCs6S+8Xv6PPK1tI\nf2kz+05doXP7EB5atpv0l74ndf63rNpVht3h4Isjv/C3L46KG5WQrKwuKKNC76QBJUbq+GhqBheu\nGrA7HBgtTl5stcECwNLtpzAYLYBE9A5vfL6l208CEorKKlm4+YTbzxduPsHiraXUm70HFa4wmK2Y\nrXYq9SbMVrvH7zTXgm/OnchgtrK4Yf7Bja7Rj///MFlsfH7Y2Qn4elZfFj/cnTqzjZFLCsXuwM45\nuRTNG8jQzm3cugOutpue98EpJvdNoPhSLVV1ZkrL9Yxfsw+90UKgSs7g17cx+Z39bp2GxEgd3aJD\n6BkbzvT+SUQGqr0+JxIjdazMS0WnVlBvtqJSXLMIraozi68JzmtXIZOikkv5Y68YfvylmqELtzNu\nVRFDF25n3+kq2oZoeO7zn5j38Q9MfmcfUYEqhndpjdVup1zv9MVvXL0XEqCNP1wgdf63DPjnNgKU\nMp/PNbVCRpBG4fOYxgGzkBj079AKi42OixMAACAASURBVM0pxPaGMRlxWGx2KurcdQ0apQyFVMK0\nnCRmDnB/Ns4ckMS0nCQUUgl3vr6dnSXlLN5a4nEPC3/POUNSCNMqUcmaPt/YrHiWN2gLwFmAsdjs\nbmsVkpOmIFgae4NgXuCHH3744Q03zDtITExk2bJlt3Itv3uolXLMNjsjurV3E481tu9zrdx54+fC\nNU7+uKx48ecCP7RDay3dYkJ5aNlupuckeXh5u9psus4dqDZaeKBnNPVNDEwSPMSn5yQx6R3ndM83\nvi9BIpGQlhDG4I6tuNxQ1Q/RKDl49gqrC04xtV8SYVolL9zbEZlUwsYfLnit8M8ckISmoar1+eHz\nXj9DoWoWGx7g9ect4ci2pEJ/sy34W8nj1RstKOUyagwWgjRORxGdv/r3q0ClkPFQrxjKKuqQABab\nw6slplYlZ9ljPcXugFwqbdZ205uQXqdWcKXexOlKA4DH+VcXnOLxdYeoMTgn6TauOAvB95pC53Ft\ngjQsH5Pq+9o1WZBJJXxywNkJEGhKwvPh/b1nyEmJFJ2/Hnx7t9i1uFpvps5s9Ti/t7kDvgSyYzLi\n2Fp8mTvaBonvqfGzLy8zjks1RrELIHxmQWoFf/viR/50ZwfyMuPEz7dx58VitROhU4m/PyYjjgOn\nrxIbHsCzn/3E8/fe4aFpmLr2AC+MuIOkKB3pCeE8vu6Qz79nUqSOS3ozH+4/x/hGGgmjxcbKXc5u\noqtRgt3ucOtuNFfN9w8I9MMPP24UN5QEyGQyLly44CEOrqysJCoqCpvNdksW93uDxWbDYnUwNSeR\ndUVnRQHim1tKWJPXm2Gd2xAbHiAOzDlTWU/rYDX3v1Xoca4gjZwIncppvdlARxASg/87sitGi52z\nVQavFqAAV+vN9IoNpej0FbHF/tjKvWiVMlbm9b6uYTdCUNstOhiHQ4JCJhU3wgq9iXqzlWGdW9Ml\nOoS3t51sUig4pV8iDqDGYOHrWX1FK8HSRpXAWqOFeov3a7C5AL2lNJ+bbcG3JImQSSUEKOU+dQJG\ni42lO056dU5R+zf/Ww6z1QZI+MplUq9r8O3qpPXg27t58+HuzOifxJU6M/Xm6xPSj8mIw2x1UvG6\nRgczIzdJHJgVFeQMXgXuOcA7hWd4a3QP4JrIeN7wDqwpPCUmBnKpqVmLykCV89oVqHvevP5d73Gh\na1EwN5ePD/zCpL7uHPimdE2+BbJOGlVMWABLRvdgWKc2xLg8+05X1pMQoWXKWvdBa0JiYDTb0ark\njHy7wGuhY9yaIjZMzgQHbvfMUx8eYcH9namqN3HsYg3hOiU4HFjtdn6+UENVvYlwrQoctMhGFQmE\n65SsKihj4eYT9LstgvT4cM5U1ZPbIYq8zHhGpcaImoDRK/awYXImRuu155dAz/TleOYfEOiHH37c\nCG4oCWjK399kMqFUKm9qQb9nKBrmBJy7UscfurdDrZAxLiue/P7J2B0ONv140aPSM7FP023gqjoz\nj67YKwbJQRo5QRqnf7ZCamP3nFy2NgTSxZdqAWflcN7wDkTo1DxzV8drU4RNVu7p2pavf7xIdTPu\nGd48xOtMVjRKmdPqr9BdaDg1J5GnB3dAIWt6GqtQ4Z/8ThFbiis8uLeu7zFQrWDFDu9Wtc0F6C2t\n0PuaKtyS+QHNJREapYzsBVuwNryOt4qe3mhh6Y6mnVMm9UnwdwRuMSQSiYejzfyNx8Rg9rND14Sw\nw7u0pnWQmoeW7hGH9LXEdtO1+1VrtFJWoWft+DSWbj/p1UpTuP4fTovm/b1nRB7/1XozETq1W7W6\nJRaVP1+ooV2oRlxnY69/b/e4s+ig5pHe0SINRzh/pE7lNWB2FcjumzeIWqMFjVLmNo8jJiwAB/C1\nl2ffpL4JVNWbxNcXAvmpaw9wuqoOi81O3+RIr4WO/P5JWGx23hrdg8CGqvylagN3d2nDoTNXxM/7\n7xuPugmb145P42q9mZJyfbPJVJhWScllPbtPVopJ4rbjFWw7XgHA+0VnmTMkhUfTYxmzci97TzkF\nyrVGC2q5zK07IBPcFHzAPyDQDz/8uF5cVxLwxhtvAM6NcPny5eh0OvFnNpuN7du306FDh1u7wt8R\nrDYbzjqjBJXc6Rjz8LI9rB7bi/eLzjTpd73o4e48uLTQLTiY0CeeI2evUlquFzfAe7u1ZeeJCjq2\nDeL7Y5d4MDWGSJ1SDCQAN+qA6/n63hbJvd3a8HBaDBIk1+0hrpBLeGtrKW94oRABPNQ7GrnNtwiw\n1mihzmwXvxaCjDlDUkRqwtjMeExWG61DvNOBGgfotUYLKhcqjdFiazK4d+0itLQF35T7j68kojHF\nqymxsVIua9Y5xY9bC4VM6pGolpbr+dOGI7wwwkkfEQZaVRssyCRQbbBQWq73GXyPzXTSQD6fkSU6\n9ShlUkIDFJyWSlnuYyjfnCEpzP34B7KSIkh/abNYve8VG8ozd3X0uKeaqsALtqRPrj/M0sdSr+se\nz8uMo1Jv4rnPf+Kfo7oyqcGCdE3hKTddk7dE4E8fHmbPXwYglUrIXrDFLeF4YcQd4qAt1/cu3BPv\nTUjHYLG50XUOn7tKpE6F3mh1W0fxpVpR9zSpbwJ1JguPrijCaLVyb7d2TMtJok2QmgPnqvn4wHm6\ntA9mak6i2H3YeaKCjw+c5/4e7QDfdKaxmfE4HPDN7L4EqRWkxYfjcHh2PEb1ikEpl4naBKFI8fyI\njgSqr9EzN0zO9HgNP/zww4+bxXUlAa+99hrg7AQsWbIEmexaZVKpVBIXF8eSJUtu7Qp/R5DLZMiA\ndqEB7CmrIC0+AqPVSkiAktUFp7zSdgS/a9d29/7TV5j+7n7efrQXq8em0js+nGqDhXCtin2nqwhv\nEApX6M1MyE5g2Y6TzBmSglwmcaMOgLvbUNfoEB5fd5B1kzKuy0N8St9EFLLmK+wOHD4DD51aTsnl\na9SfMK2SgtIKpuUk0aV9ELkprcTZAaNSo6nUm30G6EaLjWVNUGkAj/fXuIvQXAvel7agqSSiKb95\nbzqBlviZR/itAW8pvDnaJEbq+MdI5wRub1qW5+/pyCMr9vLy18V8NDUDqUTicU1M7peABJA3VHzP\nVNWz+2Ql93dvT4+YUCa9s8/rely5567V9qo6M0Wnr3gNvoUK/F+GdSC//yCRmrfjRDkPvr2bK/Vm\nKvUmnw45h89eoVdsKBdqDOSkRImDu/527x0ASCUShnVuw7ScRKrqLdjtjiafGXmZcRw6c4XU2HAC\n1TKqGij+CZFaogLVTSa6wj1RWFJJZnI4M94/wIzcJBY90p0ag4VgjRxJwzqm5lzTNQgJ1syPfhQ7\noKL2KTuBbu1DuK1VYIPu6QRtgjRcqDHQ77YoxmbFo5TLyE2J4q2tJ1n2WE+PGQ+CjepTHx6iVaCG\niX3jeXRFkWgJ6kpLGrmkkPWT0+ncLpiT5XWiMHjR96V8+cNF8Tnqt/n0ww8/fg1cVxJQVuYM4nJz\nc/n4448JDQ39VRb1e4XRbMUKWG0OVu48TY+YUNaOS8dosfHqyK4iH1ioSgl8+PJaE//4z3FG9WpP\nmFZJXLiWJY+mYrbasdkRq4NCkNu1fQihAUo+O3Se/Nxkag1mclKc+o6mhG7CFOGkyEBqDBan5z+e\nVXBvHuIPp8VQUdvMcDKjhUvVRp8Um71lVaKL0NyhKeLnIZNKeH9iBg6Hg3NV9bQN1RCglPsM0GuN\nFpb5oNKMz07wWIc3mk9TLfiWagsar3FL8WWvfvPetAyCc4ovP3M/bi282U56E726Vuon900gTKuk\nQxsdGoXMqz+/TCLhqQ+PUHyx1o17PzottkX0O4F77ro2X9Sf0nI9h89dRSGT8O5u56TuF786Jt5X\ndUarmAy7Brh/u+cOBt7eCpVcRnJUkNg9qzWYCQ5QIZU6aY2z1x/k6SG3I5NKCVDIUMplTM1J9Jg7\nMHvgbTzQsz0qudMe+T+P92P78XIWbComKVLb7HuvNVroEh2MTCrhvQlpXKh2zlWw2BzYgcvVRmLC\nNMilUrQqJxUyQqfCZLFxpqre7XxC96zebGVr8WVG9Yomv/+1zs6lGiPbj19mUMfW/HlwCoEaOZdr\nTEzq4xT81pmtaJVyaowWSi7V8o+R3ag1WAjVqjyE465/40C1ggX3d2ZKv0RiwjTY7Q7qLHY3qpXf\n5tMPP/z4NXBDmoAtW7YAYDabKSsrIzExEbncP+DoZqFWynHg7LS8NqorIGFXaQUjurfjyLlqr3zg\ncWuKCNeqePau21lVcIrJa/eLx0zrl8TYrDhykqP4/MgvYpAbGqBkzdhehOmUVBssPD2sIwazDXMz\ndJwrdRZW5qWyYmcZL379syi4E+gHh85e5cjZq7w/Md051VQpZ//pKo5drKFHTFizAetTGw7zzwe7\neR1QNC47nvsWF/j0Wp/UN4HosAAx0PfFkVW1gEoztFMrvv7xknj+qTmJLRbbtlRb4LpGs9XOUx8e\nbrHY2Gz19B8XkJfpFJX6EhP6cf0wWd3pYs0N8xO0LF/PyiZAKWdRA7WlcTA4c0ASTw1Ooc8rW8Tf\nrTFYqTaa0TajH/HGPRfw8tfFfDjFe/chLzOOB5YUcqXeOaBsw5QMVu0qE++r4V1a89zdnZiRmywm\nqXaHw6nr8dI9m73+IFE6NTMHJPP8PZ08OiNPDLqNiQ0Bs3A+YahYY1rShikZPLn+EMHNJLqCdiY6\nTMPa8Wl8cfiCR2dtUt8Ejl+q4V+bSzhRrudkeR35/ZPcaITC5y2sa1jnNk2+T6lEgqNhwIDdRR9n\nszX83wEd2wRhB8w2Bw4c4nTn0nK9W5dUsCp9aFkhOSlRjMuKJ0Ap56RLEUA4xn8v++GHH7caNxS5\nGwwGZsyYwZo1awA4fvw4CQkJ5Ofn065dO+bOnXtLF/l7gdVhx24Hg9lGtcHM54cv0KldEG9tLW2y\nyvjyfV0wWGyscqlEulbKr9Sb+ceDXbmraxsWbComJEDBH7q3Y9WuUx5BwdScRLpGB3P4bLXH2oI0\nciIClaxw4Sa7Vrbu7daWpCgdk97ZT2y4hk2P92Xq2v28OrIrqwtOUW+2+eTPVtWZ2FJczgNLCkW7\nwQq9idAABTYHfHHoPKXletEWsanPY1jnNrQP1fisnFksFmqMzQ8hemNUdy4Nd1qaFpZWIpU0L84T\ncCMWotcrNtapFV6rtX53oF8PqoaKNjiTuaZErwKEv/Wqnad44s4UMfFsLLYVHbjSY9lZWoEECfOG\ndyBIraTOZPV5XdjtDr6amU2wxpN7rlPLUMolDG3oPgjal1OV9QhXc1WdGYPFxuoCd91BvcmOyWoD\nFEilEkxWm8/u2dwhtzPzg4Mo5VIP8XSNwcpznx/lSp2ZiX0SUEglYgLQ+Lg3NpcgQcLro7ph8pHo\nulIPF9zfWZwd4nou125M0ekr4mfelCVrkFqBwWJj+U7P97nxyEWGdXIOO7PYHEiQEBmo4qMD53n1\n22KP+6/4Qg0jFheIz9dPp2cyesUeDp+tdktQLDY7R87VcORcDQ6Hc+JxvcnmYWeKnw3khx9+3GLc\nUBIwd+5cDh8+zNatWxkyZIj4/YEDB/Lcc8/5k4AbhMVsR66QEqRREKiR8+mhc0zu17fZKiNc881v\nqlKelxHH5zOyAAdLt5c1SVNZcF8Xhizc4fFaYzPjsdocLNnu7rojBDNLtpeKm+q93dqx43gFj6TF\niAG7sC5h3a6Vuqk5ieBwsPTRnizYVMyhs1eRSpxVzHu7taVz+2DuvKM1Vw2WZquuU3MSkUulPr3z\nHRIZQRpZs52JNzYX883Rcsr1Jh5NjyUjMbzFjhsttRBtLBye2DeeMK3SLajw5fctBR5Lj3PzH682\nWPy+IL8S6kxWpBJESo/gwNOclqW81uRGbXHtBIQGKJk7NAWAmQOSeeaujpitNj48cI7U+d/SMyaM\nt0b38OiQ5WVeu3esdmdt+ugvNTzcO4b8/s7rQaOQ8eH+c7QNURMTHoDV7qxWn6uqZ8/JSuYMSeHl\nTcfQquRunbHclCjeGt1DrIZH6FRsmtW32e5Zfm6Sm3ja9X12bR/Cg6nRzvvSaCFI6SmyFiB0y+x2\nu9dE11U709JuTFKkjr11Tgcebw5HwjRgtcKzS+j6XPUmpv534WlqDHq3pGhinwQxyXAVMlcbLIQE\nKERtgkPqEI8T1vrBxDS0DWLnZz//kdce7O71vfnhx/9PxM3deFO/f+rl4bdoJX7cKtxQEvDpp5+y\nbt060tPTkbhUR++44w5KS71bM/rRPNQqOVa7jXqzFavdgVoup6rOd5WxvNZEgEqGuoGO1RQ/+Y3v\nS0DitI1sjqYyZ0gKb20rdd9ws+KaFaJW1ZmZNSCJUb1i+PjAOUZ0byduzK52gII4LipQzamKOk5c\nquWRFXsYkxHHp9MzsTscPLZyL4M7teKhtBhmvX+AP/Roz2MZsc16rV+psxChU7JsR5lbp8O1Ol5j\nsKCSS31SaYwWG//acrLJILwp1x8BLanqOywOr8LhqTmJPNQ7plm/b1fdQUKkluRInUh3mDUg2cNN\nyI+bh2uVWwhw5w7t4LPLtbesioPnrhKscff7r6pz0nDMVjsfH3QG/I2Dy3cKz3hYf7oKSz8oOkNC\nhJb5G49htFoZ0a09U/olYLPbsdntKGQK7u7a1o3mI9zTY7PiCVDI+I8X29/GrjwZDeYCzXXPesWH\nUWO0EKlT8+rIa7qdSJ0KJIhJRUuGll2uNTLrg4OMTo8VaURCouuqnUlpFdiibgwujTyBRhUcoCA2\nXCO6A0mxe33ONflc3VyCw4EHtUhIirpHh7D52GXg2vM1QHEtRbfY7NSZbGIyIqx1W/FlFn5/grzM\neF6+rwsAlXqT12eNH3744ceN4oaeJOXl5R6DwgDq6urckgI/rg9Wm83pEKRwIJFIMFqdXHtfVcZQ\nrQK5VIpKIWm2IiZsTEmRgRw4c8Xj50JS0bV9CHv/MhC90UqAyunbPWHNPt4Z39vnWqIC1USHBTB1\n7QEm9on3CNhLy/VuFKJ/j+9Nu1A1FquDSJ1adCEanx3PhsmZmG3OQGb5mN6YbTa+OHyBe7u187mG\nyEAVK3eWuXG2I3Uq1u4+gwQJE/vEE6RRYLd7Fz668n73PzOQQLUCg9mKRCKh3mwlQClv0UTh5ixE\nHQ4Hi7eW+hQON+f37ao7OFle5zY59XqnDvvRMnizCP3k4HleeaCLh0uMUKl/7rMfUEilXK038+74\nNJbucPf7FwL+tbvPuFWTHQ6YN/x20hPC3Kw/XTsI84Z3ID0hguVjUkXDgHX7zhIdGsArm4pZ38Dz\nb4omMz47nqtGixv33psrz4lyfbP8/CC1glFLC3lvQpqHvmBlXiqHzl69rqFlYVolZRX1zF53mFkD\nknmwZzs+OfQLk/omumlnfFmQCucKVCtEZ7HESB2LHu6OXCpl8cM9RHHzd0cvMviONm5dwjCtkqRI\n3XVPexaSohDttbk5QoC/8LsTfPnDhYaiSTKT+saLdqvCWqvqLMRH6Lina1sPBzP/JGA//PDjVuGG\nkoDU1FQ2btxIfn4+gBj4L1++nIyMjFu3ut8ZFDIZepMVhVSCRCphRLf27CqpYGxmHAub4MSeqayn\nTbCG9ZMzOdDAefVZKa83s2ZsL8asKvJIBISk4u43DxKolrHp8b4Mf2OnGFz68sUekxHHzpIKxq12\nWhmerKjl29k5XjfmqjozVrudkAAFDy3dQ1ZSuFhJE4JXg8mKQiFFb7ahkEk5V2VgaKfW7D9d5bPC\nbrbZWLytxMNBKFzn5PUr5TLMVhtLd5TRpV0IE7LdK4xGiw2pw44dKRt/uMCKnSc5XWkQN9+JfeNZ\n5oNO5Vp992UharbaWyQcbgyhAyF0i65Xd+DHzcFblTs0QMEHRd4r9euKzvLXuztRb7YRoJSxdLt3\nTr23avKawlNMy0miqs7k5vgjOGQJ9JTGMz3GZsWjUUhJbqVFo2h+AF9VnZnKOpN4XyVH6jyq/ifL\n67hca/TZPbtUY+TgmatU1plZv++s+JwI0ypJTwi/rqFlja2GhXvixCW9xzCylpzLYnM+bwQB8fJG\nnUKhA6c3WVHIJDw5KIW2IWqykiKoMViue9qzkBRdrTeLyYEQ4AsJgOv7CglQUFVnJq9hrff1aMdD\naTFer5em5ob44YcfflwvbugJ8uKLLzJ06FCOHj2K1Wpl4cKFHD16lIKCArZt23ar1/i7gblhNPyB\n01V0iwllYp94PjpwnrFZ8YDEq+uFze7gvrcKKNcbmdovkdQ43y48oQFKrtSbeH9SGtuKy0WbUXDf\neKvqoFJvRiG9VoVesu0ky8ekAp68fldv+zCtknFZ8ZTXGpueTJoRx66SSg6cuUJJea1bJe1qvZlg\njQKHA6x2BxLAAchlEkICFIzNigM8K+wT+8RTa7ASqVM36SCUmRQhOgMJn9H4rFhyU1qxpfgSGw6c\nZ9+8QazccZJX/lMsrrfGYOWd3aeZmpN4XcF7Uw5FNyIcdu1AyKVSds7JbZHuwI9bhyC1Qqxy/33j\nUVoFalgzrhf//Pa4R6VeCPxGprZn1NJCvp7Vp8lrp6lqcmWd9yp3c7ak47LiuaNNsNe5Bq7H1xot\nLH8sFY1SxoQ+8dgdDrYdv+y16v/XT3/irdE9AO/ds6lrDxCmVRKuVbl1ERqLp4XPSPDZF95/U88T\n17W++IdOgMNtCFiNwcqbW0pYOz7NwwVJOJfZamf9pHTUCpnXKdsLN5/A7nDQuV0w0aEaRvWKZvHW\nEp7ccLhF91q4ViVW84VOA8Bzd98hdmguVBv56Xy1kxoFbvSfQR0iUXVpw7ScJEwWG58d/oVRvaJv\nqFDghztulsfuhx//m3FDSUB2djaHDh3i5ZdfpnPnznzzzTf06NGDwsJCOnfufKvX+LuBoqG9u2Hf\nWdISIjCYreSmRBLQsEG7VpTNNjs/X6hh7kc/iEH8gk3F5KZE+RzKs+NEOePX7POwGe2bHOm28bpO\nBXWtqlfXW5jcN8EpPGwQ3ZaW6xm3pgiAlXmpZCZGoDda0ankTOnnpMS4bvJjM+Mbvn9NDKw3WekR\nE8reU5WEBCixOxxU1JkYs7KIcr2zAjmpTwLxkVre3nqSvMw4pucmiRV8s83OtLX7WPJoKvOGd2gy\nQJJKJIzLinfbzFfsOs2KXafFr2uNFjYfu+Tx+UXqVJTXmq47ePeGlgqHBXibO9DcxNLGbkJ+3DxM\nVhufH742TbbGYKHOpUrc2PWnxmDlar2ZoZ3aUGu0Xnc1OUyrZM/JSre/c0uFsD9dqGa8OqHZ68xk\ntiCXSnloWQHTc5KYmpOI0eLpyrOl+DJT1x7glQe6uPniC5N6txRfJqVVIBV693tEoOs01kMIgw0z\nE8PJ7++0DdUonfTDxrMyhLWKVqV2u8cQsF+qjYzPdj5bKutMYjdm3JoiNkzO5J/fFDNzYHKT4uY1\nhafY8/RAbHY7i7aUuN1Xvu61vMw4KuucSV/X6GCvnQZxCKEDN+rWoq0lBKoVPHFnB7YeL+f9vWd4\nMDWa+3u0RS6V8o8HuroVauCa+xv4dQJ++OHHzeGGI4TExESWLVvG3r17OXr0KGvXrr3uBGD79u3c\nfffdtG3bFolEwqeffur287y8PCQSids/VzcigKqqKh555BGCgoIICQlh/Pjx6PXug5aOHDlCnz59\nUKvVREdH88orr3isZcOGDXTo0AG1Wk3nzp356quv3H7ucDh49tlnadOmDRqNhoEDB3LihGegfVNw\n2KkxWjhfbRJdKnRqBXYHLN9RxqDXtjL13QNMfqeIfxecIiFC63GKOR8fYWpOIrMGJBOkcW4KQRo5\nMwckMSYjjhe/OgZcC4r/XXiadyek0aldsNvGOzYzntOV9YQGKFk/OZ2zVfXM++QHHlmxh8wF37Ny\nZxlKmZRPD56nTZCaDZMz+XJmNofPVtP7xe9Inf8dvV/6jvX7zjKpbwJF8way++kB7Js3iH63RXDX\nv3aS9tJmfjhfzYYpGYRolLz6QBeeHJTC1uLLDH59G98fu8yHUzKI1Kl5Y3MJy3aUYbfDjP7JNJic\nYLXZWbTlOHqjhe0nKtlbVklmYgQXqw0suK8zd3Vp7fb5rCooQ6OUERuu8fonEIKNsop6j5+5co9d\nEaZVktIqkNhw39akrhCEw94gBPCu8DZ34OWvi8nLjCO/f5Lb33rWgGSm5ST6g4JfASq5jHu6Oud2\npL+0mWFv7CRAKfO4JsAZrK3MSyVCp+bR9Bhx0Jg3uCbdAvIy4yg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T7lF83dIy7p+L77Vr\n185jmZ49eza5v2q12muQuTlYHXacThnZafH0iA1hVZ73IJ148x+b0o7xPWPJXrUPg8kmDa4+lJVE\nWcO/T+F6cB84U8kd7+ZLXFvxAZZfWI4gkxGiVRGoVnhUG1vrEHq5oer00oYTfDE7rUmd7jvf201N\nvY3LtSbOVNSxMrsfFUYzv16q5ZU7e2Gy2tFplHw8fQBd2gazshHXPjstniGdoqiz2DzoT9CgcmKy\ntipI2XLiEiNuaMOtvWLIzoin3nJlkLfWZEWtkFNdb+XzWRmYrC6FJnEg+lKNiWe+/ql1g5mNVIJa\n4zLcGrSWPuRH63C116hM5mR2ZiJOnE0GeYWlBvYVN29qd+x8Fe9tK2LW0ASOnKv2GtgXlzt0tpKU\n2NBWJRRKucDnB85J8qViAn3wdCXnK+uICw/EbHMSFaTyGAJuvL7QABWny40eWvxVdVbMNm/ZUBGT\n0+Mx2+zsK6pixA1tvbpkMvDS9v/bbd2apfU8f2s3Bi/Z4mGm9tLGX1p1X7pvYAccDmezCduOk6XI\nZDAutb1XUF1YauDQ2Sr6dQxnd2EZw7q0YXdROW/+eMqrMDBveBJWuwO1QoYTMFltTG80g1FvtbNy\nZ7HX+RaDdNFZ2Necl/t7vuZPmksy/hf8Qq72GvXDDz9aj2tKAvLz89m1axcbNmzw+uxqBoOvFufO\nnaO8vFwKxNPS0qiqqmL//v306eMynfnxxx9xOBwMGDBAWmbhwoVYrVYpWdm0aROdO3cmLCxMWmbz\n5s08/PDD0rY2bdokuR/r9Xratm3L5s2bpaC/pqaGPXv2MHv27Ot3gE4wWe3kpMcToFY0OxA8OzMR\nQSajzGCSlHJEuTqtUpAeIu5qNjPW7PdQu5k0oAMBKjnJ0TrqrQ6Ph2prHEJNVjt1ZtffurDUwIaj\nFzxUicRKmRgYie3q1zefosxgYeaQBF774SSltWbGprSjTbCG6CB1sxr/0wbrUStdMyjuVcOfzlfT\nqU1Qi/vbLlTr4ZWQk65nTmYiDpAUmfp0COfd+3qzYmeRTxUUQSa7Ko6+L43/a5kf8OP/ezidMtYV\nXFHNMZpshDTw7t3VZp5df5zPZ7nuH76caQWZjBdvTyFYoyS5rYvy9vKmEx7LzRiSwL0rdvP2vb2b\n/F3npOtdalZ2lwLP+F6xfHngHAMXbaam3tasbn1jhRzR5EohCMSGBSDIkLT4a+qtOBxOacDeV+Xe\nYrWTkRTl1SWL0qmpqbcRpJYzbZDLY8RgsqLTKFucMUiICqSo1CgFwlVfWlu8L12uNTFo8Rbp2Bv/\nDRoHzE0F1TkZejQqOTq1AovNwdQMPWNT2tExIkBKsC5Um4jSqVHKBWpNEKCSoVbK+Xz/OVbtKvbq\nxpTWmlm794y0v2KQfrDBvd3XnFdjrv+d7+3mrXt7Sd4KQRql1+9PhDgj1hrFMj/88OOPh2uKPObN\nm8d9993H008/7cWTvxoYDAZOnbpyIy8uLubQoUOEh4cTHh7Oc889x4QJE2jbti2FhYU8/vjjJCUl\nMWrUKAC6du3K6NGjmT59OkuXLsVqtTJ37lzuvvtuYmJiALj33nt57rnnmDp1Kk888QTHjh3j9ddf\n59VXX5W2O3/+fIYOHcrLL7/MTTfdxCeffEJBQQHLli0DXInNww8/zN/+9jeSk5MlidCYmBhuu+22\naz7+xlDI5QTJBf782WGeuvmGFgfAtCo5Gx8egtFsJ1ClYPvJUiYuzSd3Sj/pIXK2op7ZblKUFUaL\nxyDcjDX7WTO1PwFqrtohFKdTmkfISdczvncsNruDD/JP86+D56XuhAj3dnVufglzh7sCCjGpWbfv\nDPcO6NiiytDnszKot9jRquSUG828uulXPtt/roEf33SQ8vNv1V5Sg69vPsm0QXoP1ZMXWqhQTh+c\nQJBG2WqJT18a/42PyY//G1DKBfaWlLNgTFcUgiCJIoi8+79+85NEqdly4jJTB+k9Te1sdj7Zd0Zy\nGL5inpfAnX1jKW9Ink+X16EQZKybPhBBEJicrsfp9DbdmzZYz6T393D4bLWHnOaHu89QU29g7rAk\nn5KS4m/52Vu68uK3vxAdrOKdSX1YsaOYrw6dY8P8IdyzfDdzMpPoGBGASilDq1LwyKcHeXJ0V48q\n96UaE49+doh/3JHKzW/u5N/zBnncS0Q5TWQylm09xdeHzjO8cxseHJbUKrphUanRIxBujXMxuOSS\nb3s7j7fu7eVSHzJaCQ9UseXEZY+A2V2RqM5iJ1CtYPuvpXxz+Dcm9G5PSmwwgWoFdRYbG45dkLbZ\nXIKVnRbPmvwznLhUCyCd7z+P6uyRBIhBughfvP7Gs1839WiLvsEjJkKnxmi2EhemlQbJG1NHXX7r\nfvjhhx/euKYkoLy8nEceeeR3JQAABQUFDBs2THot8v6ys7N59913OXLkCLm5uVRVVRETE8ONN97I\nCy+84NEa/Oijj5g7dy5ZWVkIgsCECRN44403pM9DQkL4z3/+w5w5c+jTpw+RkZE888wzkjwoQHp6\nOmvXruWpp57iL3/5C8nJyXz11VeSRwDA448/jtFoZMaMGVRVVTFo0CA2btx43TwCoIGOohS4oV1I\niy3v8EAVMmRMz93H+9n92FdSzsw1+6XPTDYb84YncUtqDN8dvSA5DouKJO4V+l2nyogNC/DimjZ2\nCHV/6G84doEbb2jLv+cNIjRASX5hOd8dvcCNN7RhcoZrUE4MfH1V32rqbZTVXqluVRgt2B3OFlVE\nakxW1h86j1wQuL13e0K1Ku5P68jDI5IpKa9DAC85RJPVjiCTUWd1eFXLbu7R1mOgOCEqkOggTYsV\nSmg9R9+Xxr/7Mf1f5+z+kVBvsfHyxJ5N8tjHdm9LWYM3h8PhoLi8jo4RAdjsDiw2O8t3FDfZERqY\nEM5f1x9vCJpVvH1vLxKjdFTXW3ns8yM8f2s3l1N3w++6sNTAbW/nSb9nMbgXqTNPfnmUQUlRTXYU\nxd/yJzMGEqhWUFhq4N9HLqBRKCg3WDCY7JIRmNnqRKeCucOSmbxqH06cXpSYnSfLKCo1evDWxc6j\nyepgpRu9cdvJUhaM7doiredkw7GJ97WQACWHz1X5vC9dbnAuPnyuis5tgig1mCksNXDXsnx2L8ji\nzR9/5dlbuvPY54c9tllYamDGmv10jNDywyOZ5KzeS68OYcwYkoBMBiNf3cm2P2eycpcnPbO5BEv8\nG7hLkIrnu1eHUApLDdI90WJzSMmOL15/TkMCuPnRoQSqFJisdn7+rYZObYNQKQQC1UrMVruXg/Lp\n8jpUcsE/M+SHH340iWtKAm6//Xa2bNlCYmLi79p4Zmaml8+AO77//vsW1xEeHi4ZgzWFHj16sGPH\njmaXmThxIhMnTmzyc5lMxvPPP8/zzz/f4j5dK4I1SiqMZiZnxLc4gHimvI42IRpWZPej1GCmT8dw\nwgNV3D+wI2argw3zh0hGNoWlBuYNT2LW0ET+9OlhSd9axEsbTvDFrDQvzm6wVkGP2BC0SgGbw8Gb\nm09x8GwFQztFk5OhRykXWLLxBNtPlhIWoOKzWWks21HEtl9LmZ/VyUMG0T3pANdDPSxQKVW3ooM0\nTBoQj0zm3ZGQzo8YGFw28PioLhJ9pzHVImf1PtqFqBmcFMWvl2o4V2kiLTGCmFCtV7t8RJc2Hqon\nyQ1BV0uJSGRD0O6Lo994APhq5wf8+P8vBEHWbJdoUv8ObDp+iTv7xuJwwndHL5Cb3zqN9weHJUoJ\nwKczB7I6r4T1h8+zcf4QXr7T5ROw/vB52gRrWTO1P3cty/f5mxKpM0nRulYloFa7A7XSRRf5YlYa\nj3x6iCidms9mpfn0wfhqTjr3vb+H749f8lDIERN88X4iUmcqjBYC1ApSY0NJjNJRWWdBKQiU1pqb\npfVcqjFRVGqUaEpyQcYbd7s09k+X16GUu2h5q3aVcN+AOLJX7ePJMZ15e1IvD0GBxRtPUGG0UFBS\nJTko+9rmrT3bu7j7k/tjMNt49YdfeXJ0V+4f2MFLeexaPFzEe8eqyf1QKQSPoeILNfXMz0pm2mA9\n972/B7hCdxTpY/VujsBd2wVisjqgoXagVsqJC9OiEASCNEoUgkBcmNafAPjhhx/N4pqSgE6dOrFg\nwQJ27txJSkqK12DwQw89dF127o8Gs81OSIAKk9VGgFLhQeNp3OJVyQUKSipY8p8TZHaKZnZmIs/e\ncgNDkqN47PPDHDhT5RHoiyoUgSrBS2KzsNRA9uq9fDx9ILOGJnpwTS12B5VGM22CtcwamkBIQGd2\nnCxj/eHzDEmO4s6+sZypqOONe3qSm3fFHGnK6n2szunL6fI6Xt98ymub7hWvh7JcHYp1+87SIzak\nSfWLnHQ9Jqudm1LasTqvxCsQExOmh0ck89f1x7m1Z3se7dlFCggcDid9Ooax/3SlpEA0KCkKuSCT\ngvTWDkRXGswE+lD48TUA/OmMNL/G//8IWpKnnDssmXGpMYCM5TuueF60RuPdYLKRFK1j+mC9NBcT\nHqiiqs7Kx/vOSOtqE6xtsWNWYbQQpHEN+V6Nbv2MIQm8dW8vLHYHq/N8+2A4cfLR1AFUm1xD+o3p\nNQDIYOOxi173rvVzMxBkUGawEBmobJa+9/w3x0mNC2HttAGcrajH7nBitV8pGinlAk5gSkY8QRql\nz6QlOy2ez2aloVUKrXJQXratiC8OnuXWnu15fFQXACYNcMle//OOVBZvPEFhqUHycGmNZr+7kWOw\nRslfv/mJjccuSvc+k9XOuhnp7Cup4HKtmU9nplFptBCsdTkSf7LvDHFhAdIck7ivAWpPfx654Hpt\nsztAKUcQ/nv+PX744cf/Bq5ZHUin07Ft2za2bdvm8ZlMJvMnAdcIlUKOxWbHZHVwsdpIflE5fePD\neHBYIoYGN1+Lzc7FWjOlNSZ2F1ewMrufVIGbNljvQQ9wh1j1G9M9hi0nyrw+H5ochcPpZE3+afYW\nlyMIAg6Hg/76CHIy9NjsNr46eJ5PCs4wLrW9lIiEBaj4ZIZrsK5xlfOFf7sMjcoMFp9KFlNy9zE/\nK5mc9HjUDU6sUTqNz0E994HK/g1upr4gVlQ/mzmQVXklXlXMFdl9+dO6w/xzYg9WN3z+3UODpepg\nUamRMoOZ2UMT+ergb15zDaIKSpYPV86mBoD/8q9jrJnaD61SzjvbTv0udaCmUG+xoRAE6kxWtNcg\nP+pH69CSVv7lWhN/+vQwq3P6eSQLpa0wkgrSKOndIZSMpEje2nKK5Q/0ISMpEoUgeCYezit+Bc3R\nBXG6JE1bKykpLjNzSAJKudBssjN7aCLPfH2MN+/p5UWveW7cDazaVeLTqVcmg9TYUKbmFrBycl+q\n661M7BPnRTfceOwij4/ujFIuw+GEbxs6Ko2LIXKZjL9/9wuP3tiJTxoSJdFfpNRglrY5sU8cFUaL\nxMd/YnQXj20azDae/+a49LlYYBD3VdymOEzdmr9nY26/KE6wds8ZgrUKHspKYkqGy7dE7OosvKkL\ncWEBiGMCMuBfB8/z8fSBUlfBfTZJHPi9Xupjfvjhxx8L1xQlFBf7bmn78ftQY7KyJq+EOcOTCdYq\nubPhwRAeqCIpWic5BtscDnYvyGLO2oOAi3v64nfHmTssiXYhagpLDR4qQBVGCx0jtARrlNySGsPZ\nyjqvB+qDw5L4ZN8ZFm884bFPW06UUW+1M31wArf3cclq7jhZxm1v5zE2pS0p7UN4b3shb9/b2+th\n2NhJVeou2OzUW+18PH2gi8cvk1FudBn71NQbPAb1RH6ryeriVGuVCqrrr1RBGx+nWFHddPxSk1XM\nF27r5qFAJA4a6iMDSU+IQKdRMnVQAuN7tSdYq2TnyTLe3nqKoZ2ieDDTxf+P0qmxOZx0bx+MEygz\nmAnWKOkRG0JilI7CUoOH30Gl0cqUQXruT+uI1eYgQK3A7nDgcDqx2BzX7B0AntKmwVrXDMSx89X0\n6RjmDwCuM1qSoRWD78ZV4gqjpUmN98QoHW/d2wuAB9LiUQgCa6YMoNRgRibDi552qtSAxWb34t2L\n18Dk9HjyC8upNduw2BySeVVrJCVz80t4aHgylfXNV7nrLHZevK27ZEQoBvzhgSr6NZOkr85z0WQS\nogIZmBAhqRg1lt0M1irYt3AEVpvDJ+9efD1raCJRQSrCAlRs+7VUSpzc6UBvbz3F7KFJkjtwZJDr\n/29vLmRuVidyVu9l56nyJvd1Qp9YDp6pvCrN/snp8RSUVBClUxOkkUvqQAKwe8FwgrVKSsrrwAkG\ns42IQDUrHujL6rwSHl53yOPvJDqqu3cV3GeT/Opjfvjhx7Xid98ZRE6/qJLhx7UjWKMEGRgtrkBW\nfAhXGC0eNvXie1E6NdtPljJzSAIb5g+hut7Kysn9MZptaJVyquothGhVlBnMRAepqW6oej2QFs+c\nzCRqzFZCtSrqLDYEmYy4sAApgHWH+MDZ9sslnvnmZy/96pc2/kKwVkmfjqEUl9V5VM5F06O8J4Zz\n+GwlKoWcPh3DsdptyJBhstjZdvIyN3ZrKwVX4qCeNFRos7Fh/hAEmYwJS3fxyYw0UuNCmDssyecD\nP0ij5PufLvg8x+KxrD98Xnpvy4nLrNt3lrv7xWGxO1i6rdCrC7FuhqtFL6qg6DQKlt3fR+omuD+0\nP505kD9/dsSj29C4OqdSCJitTt7ZWvi7qncmq91rNkKkC+w/XUnPDqH+AOA6ojmtfLGyfqrU4LNK\n7EvjvSmFGVH5Z+YHBSy9v6/HuiqMFo6cq2LW0AQvycrT5XXoIwIQBIE+HcMIUCmY+F4eczKT2L0g\ni+p6KxGBTVB4EGlJrvuVqhxSAAAgAElEQVRCS12L+1bsITU2hEdHdZZ8E1xzCC1TlZKjdB6JUlGp\nkaJSo8dyRpMNnUbp5ZYsQqQ4zhicgMlqZ2V2P5/X48rsfpitdr6fPxidRonNbudyrZnZw5Oprrf6\nTACGdY7mhdu6oRAEFozuQnCDT8hLG39mdmYi4YEq3tpyig+nDvDyRnlkRCfu6BOLWiHno2kDpMTc\n6XRglwmSo/O5ijr2FJUzsW8s9w2Ma1IaGVzdGfeugjhfEKQWkAvNu5f71cf88MOPpnDN0cEHH3zA\nP/7xD06edFUbOnXqxGOPPcb9999/3Xbujwazzc6UDD1alQK1QvB4CLtX+2wOB+GBLoOv5Q/08XIV\nFqkzTic4cWIw2wjSKLjrvd2UGkzS5/mF5byy6YTkM+De7nYPDkQqUd/4cOm9m3u0ZXBSFGarlZcn\npiIXZLwzqTfBWiW7C8v527cuI6J7+3fgiTFdUCnkpCVFsXRrIf/4zwkpgI8J1dC9fQgWm50Fo7uw\n4F/HpG1UGC1UGC3Mz0rGanNSUl6L2erk6LlqPpw6gGXbi7we+B9OHYDN4eDwuRqv8yZ2CmpMVjQK\nz5/+uNQYisuMfOvmmiweu1hR6xYTTEKkjp0ny5g1NKHJh7ZWKWfR7d1Zt+9sk+uaPljfrFJMa6p3\ntSYry3cUNTmkOm1QAgrBP2twPaEQ8Mljd6+sN1X1Fztjb93bi7nDXZrxoVoly3z8DUX32PsGxvtc\n1/IdJbx7X28PycpgrYJHR3YmLlyLCrA18OdfmdiTSzUuR1yxVKPwwRdPjNKx8KYuBGqUzQoT5KTr\nsdodJEQFsHxnMUaLncdGdWbusGTMFhtqlaLFbsnJRolS4+tUTDSacksWz1OtycrjXxzh9bt68t52\n7xkG946BkwbevEzO14d/4+tD59kwf4jXvjblZJyTrufliT2xWO1s/XMmGqWcvcUuyubMoQlU1lkJ\nD1DicOKzkOAa+s2X5Fyz0+KZkqFHLZfTXx/BQ58c8nmcYrLT+DwGa5T8+/A5+ukj/epjfvjhxzXh\nmpKAV155haeffpq5c+eSkZEBwM6dO5k1axZlZWU88sgj13Un/yjQKOQ4cfF4z1bUkZ0Wz3dHL0qU\nEvdq38HTlczO9B2IigFESvsQ/vz5YYk/+/y4G5j0/l6JFtM9JoTT5fXS98T1LLypK4s3/CIplSy8\nqQtBGiVVdU7ynxyOxeaSwRQddduFuAKKQJWCKqOVtMRIvpydznfHzjO+VxzvbD1FWkIEu4sq+PeR\nC5LySeMK+ezMRPonRPCnTw956J7PavAQuLtfBz6c2h+tSs7yHb4f+IJMxvTBeuLCtSyekOKzUxCs\nUWKyXXlo9uoQSqBaQYeIgCarjqvyitn3lxH07RjGtA/28+G0/l7KIO70nzqLnZwMPUnROt7bXuTh\n6vzVoXPMGZb0u6t3rRlSrTdZUfkDgOsGGTIE4P6B8R6c8h0nSz2SZ/eq//rD5yXTqJt7tCM2TMu0\n3H2YrU5yp/Rv8m+Ym1/CngUjWPDlUZ65uavHuqYMivdSKYrSabi5RzuWbfdWzZo2WM89y3d7XFfu\nCX9ilI5PZw4kN7+E8AAlidFBTBusx+F0etGIpg3W43A4yewUzSf7zrF27xkidCpmDkngl0u1dGob\n3OIcgigl+siITsSGab2u0/NV9VjsjhZnH3QaBcVlRpQKodmOwdzhSQxeso1/TuzBobNV0nnzlWD9\nfXx33t1a2KTwwPTBekw2Bxpx+LahGy6Xgd3hZNkOb/qSeE+ek5nEjDX7idJpXMprKrnUoXUfPHaH\nGMgnReukjrA4X1BYWsfolJa7Nn744YcfvnBNScCbb77Ju+++ywMPPCC9N27cOLp168Zf//pXfxJw\njTBZ7TgAg9mKPjKQWUMTmDEkwavinZOuJycjHrVCaFGiLlKn5j8/XUKrlPNAWkdpuEzku7pL2Inf\nmzc8mc6T+xIeqMLhhPe2F3rwVEW6yZcHz3mYHonV0LuW5Uv8+U/2neHD3WeYMyyJmR/u5+WJqc0m\nLjeltGPdjDRMDWZgO0+VIZfJ+CD/NBVGC3f1jSNQULQYQH80dQDvNdEpqDSamZKhl1SLesaGYjTb\nMJibpzEYzDae+OIoRrPNi/MtBlG+kpuPpg2g0mghJMA1X/BpwbkWB0xbU71raR01Jish/gDgusLq\ncLJ8hytAFHnsi+/oweFzVR7BW2GpgT9/doQld/Rwafs3zGuUGcwUXjbwfnZ/auotGC3N/+bqLDae\nHNMZrUrBlAy9h3LXnEa8+yfHdG722hIDUPdu0V/GdmFqbgELb+pCbr5LcSshQssNMaEeNCJ3B/D7\n3t/DZzPT2VdS7nHdG8w2vjn8G0/FhZGToQc85xBEEy1xDmHtnrNSxd39mhHvL1a7neMXapvtSOwt\nrkApCFTXtXAt1LuC6IEJETy87krFvTFFK1KnJkKnbvH+Mu7tXZQZzFKC9efPDqFVKvlbw7yRL4j3\n5N4dwlj+QNNUwsadWDGQbxes8Tg/gkzG6Qpjs7MJfvUxP/zwozlcUxJw4cIF0tPTvd5PT0/nwgXf\nXGw/WoZG6eoELN7wM38d152aehufNEEpcTidTB2kb/LBF6XTYLY6+HbeYMoalCycTpjYJ5b3thf5\nlLAT13+51sTU1QU8MaazR9VM/Fx83T0mRNq+eydBDDZkyOjTMZQhyVHUmlzqNS1pa8/OTGTVrmJu\nvKEN7VUBhGgU1Dc4h67OK2HesGQu1ZpaDKB90XrcOwU3pcRwV78O7DpVxtcHzxOoVqBWCi1W1LK6\nRrF0W6EX57ulAMy9K/OPO3oQqG6eMtGa6l1LQ6rBGiVWhwOlPwC4bnDvvog89oiNLtlJGVe44alx\nIbxxT0+fXP+cjHjqzTaUggytuvm/obuE5+T0eCan63nii6M8e4uno/i16NavziuhYOFI9v5lOKE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1zW0LCs9TpW4N0HCv8f9s4zMKo6//qf6TPJJCFlElpCKgGR0EsCCaGJCCpKUdEV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H+6jqUb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\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2swMXEdxuZo/I9HlmVZusLFh/wKf7B23P5ZVbK1k68bqgSYbkCeAlERpWBwoj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\na2T+H2gOQXtJ4aWvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 750x750 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTFchqROCuhD",
        "colab_type": "code",
        "outputId": "ade35558-a4ca-4aeb-fd45-7e6cb5e773c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "df.set_index(['price', 'year']).sample(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>manufacturer</th>\n",
              "      <th>make</th>\n",
              "      <th>condition</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>fuel</th>\n",
              "      <th>odometer</th>\n",
              "      <th>title_status</th>\n",
              "      <th>transmission</th>\n",
              "      <th>drive</th>\n",
              "      <th>size</th>\n",
              "      <th>type</th>\n",
              "      <th>paint_color</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <th>year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>31824</th>\n",
              "      <th>2016.0</th>\n",
              "      <td>toyota</td>\n",
              "      <td>highlander limited</td>\n",
              "      <td>good</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>other</td>\n",
              "      <td>28143.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>compact</td>\n",
              "      <td>other</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <th>2008.0</th>\n",
              "      <td>ford</td>\n",
              "      <td>f150 supercrew king ranch</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>130000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>truck</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12500</th>\n",
              "      <th>2011.0</th>\n",
              "      <td>ford</td>\n",
              "      <td>f-350</td>\n",
              "      <td>salvage</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>170073.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>brown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3700</th>\n",
              "      <th>2008.0</th>\n",
              "      <td>pontiac</td>\n",
              "      <td>g6</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>123000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>fwd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>grey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11950</th>\n",
              "      <th>2012.0</th>\n",
              "      <td>chevrolet</td>\n",
              "      <td>silverado 1500 lt</td>\n",
              "      <td>salvage</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>134497.0</td>\n",
              "      <td>rebuilt</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>mid-size</td>\n",
              "      <td>truck</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             manufacturer                       make  ...    type paint_color\n",
              "price year                                            ...                    \n",
              "31824 2016.0       toyota         highlander limited  ...   other         red\n",
              "14995 2008.0         ford  f150 supercrew king ranch  ...   truck       white\n",
              "12500 2011.0         ford                      f-350  ...  pickup       brown\n",
              "3700  2008.0      pontiac                         g6  ...   sedan        grey\n",
              "11950 2012.0    chevrolet          silverado 1500 lt  ...   truck        blue\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DimvXJbPFThF",
        "colab_type": "code",
        "outputId": "75e31cb3-4930-445e-b118-e3aaa0946075",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "df.sort_values('year').sample(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>year</th>\n",
              "      <th>manufacturer</th>\n",
              "      <th>make</th>\n",
              "      <th>condition</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>fuel</th>\n",
              "      <th>odometer</th>\n",
              "      <th>title_status</th>\n",
              "      <th>transmission</th>\n",
              "      <th>drive</th>\n",
              "      <th>size</th>\n",
              "      <th>type</th>\n",
              "      <th>paint_color</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>293459</th>\n",
              "      <td>24400</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>camaro ss convertible</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>47611.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>compact</td>\n",
              "      <td>other</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135364</th>\n",
              "      <td>15995</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>malibu</td>\n",
              "      <td>like new</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>20034.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>compact</td>\n",
              "      <td>sedan</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128088</th>\n",
              "      <td>5200</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>toyota</td>\n",
              "      <td>tundra</td>\n",
              "      <td>fair</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>242000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>truck</td>\n",
              "      <td>silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204654</th>\n",
              "      <td>6900</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>jeep</td>\n",
              "      <td>liberty limited</td>\n",
              "      <td>good</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>161000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>compact</td>\n",
              "      <td>coupe</td>\n",
              "      <td>orange</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413959</th>\n",
              "      <td>21000</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>mercedes-benz</td>\n",
              "      <td>m-class</td>\n",
              "      <td>excellent</td>\n",
              "      <td>4 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>78329.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>compact</td>\n",
              "      <td>SUV</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258151</th>\n",
              "      <td>15350</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>mustang gt premium</td>\n",
              "      <td>like new</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>73000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>coupe</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>485941</th>\n",
              "      <td>24532</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>silverado 2500 hd</td>\n",
              "      <td>like new</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>64933.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231069</th>\n",
              "      <td>5950</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>mercury</td>\n",
              "      <td>grand marquis</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>other</td>\n",
              "      <td>84636.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>sedan</td>\n",
              "      <td>green</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253139</th>\n",
              "      <td>17950</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>ford</td>\n",
              "      <td>expedition xl</td>\n",
              "      <td>excellent</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>69000.0</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>4wd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>SUV</td>\n",
              "      <td>blue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235857</th>\n",
              "      <td>5500</td>\n",
              "      <td>2004.0</td>\n",
              "      <td>gmc</td>\n",
              "      <td>savana g3500</td>\n",
              "      <td>salvage</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>130000.0</td>\n",
              "      <td>rebuilt</td>\n",
              "      <td>automatic</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>white</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        price    year   manufacturer  ...       size    type paint_color\n",
              "293459  24400  2015.0      chevrolet  ...    compact   other       white\n",
              "135364  15995  2016.0      chevrolet  ...    compact   sedan       black\n",
              "128088   5200  2006.0         toyota  ...  full-size   truck      silver\n",
              "204654   6900  2008.0           jeep  ...    compact   coupe      orange\n",
              "413959  21000  2014.0  mercedes-benz  ...    compact     SUV       white\n",
              "258151  15350  2010.0           ford  ...  full-size   coupe         red\n",
              "485941  24532  2013.0      chevrolet  ...  full-size  pickup       white\n",
              "231069   5950  2008.0        mercury  ...  full-size   sedan       green\n",
              "253139  17950  2014.0           ford  ...  full-size     SUV        blue\n",
              "235857   5500  2004.0            gmc  ...  full-size  pickup       white\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_U9CfxdhoHtt",
        "colab_type": "code",
        "outputId": "acb74148-8e97-4752-b6b4-3a89cef4fbb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 375332 entries, 0 to 550312\n",
            "Data columns (total 14 columns):\n",
            "price           375332 non-null int64\n",
            "year            375332 non-null float64\n",
            "manufacturer    375332 non-null object\n",
            "make            375332 non-null object\n",
            "condition       375332 non-null object\n",
            "cylinders       375332 non-null object\n",
            "fuel            375332 non-null object\n",
            "odometer        375332 non-null float64\n",
            "title_status    375332 non-null object\n",
            "transmission    375332 non-null object\n",
            "drive           375332 non-null object\n",
            "size            375332 non-null object\n",
            "type            375332 non-null object\n",
            "paint_color     375332 non-null object\n",
            "dtypes: float64(2), int64(1), object(11)\n",
            "memory usage: 43.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxisPIxuFQ_x",
        "colab_type": "code",
        "outputId": "7bfdf7b8-f760-4d2d-9b12-99f8e065bb9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "df.drive.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4wd    175113\n",
              "fwd    136163\n",
              "rwd     69686\n",
              "Name: drive, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMLU-LAyosWZ",
        "colab_type": "code",
        "outputId": "077d32cd-46fa-4135-bca8-1e1cb9615c7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "list(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['price',\n",
              " 'year',\n",
              " 'manufacturer',\n",
              " 'make',\n",
              " 'condition',\n",
              " 'cylinders',\n",
              " 'fuel',\n",
              " 'odometer',\n",
              " 'title_status',\n",
              " 'transmission',\n",
              " 'drive',\n",
              " 'size',\n",
              " 'type',\n",
              " 'paint_color']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlPYOhYr6bXI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv('cars_cleaned.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}